Read-only audit of MCP definition language across an existing surface — tools, resources, prompts, server instructions. Walks every definition file and checks 15 categories the LLM reads to decide whether and how to call: voice & tense, internal leaks, audience leaks, defaults, recovery hints, field descriptions, cross-references, sparsity, examples, structure, mutator observability, unit-bearing numeric names, validator-enforced constraints, annotations truthfulness, single-line strings — then a cross-surface pass: naming taxonomy, parameter vocabulary, tool overlap, instructions drift, length outliers. Produces grouped findings with file:line citations and a numbered options list. Use during polish, after a refactor, or before a release. Complements `field-test` (behavior testing) and `security-pass` (security audit).
التثبيت
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
Read-only audit of MCP definition language across an existing surface — tools, resources, prompts, server instructions. Walks every definition file and checks 15 categories the LLM reads to decide whether and how to call: voice & tense, internal leaks, audience leaks, defaults, recovery hints, field descriptions, cross-references, sparsity, examples, structure, mutator observability, unit-bearing numeric names, validator-enforced constraints, annotations truthfulness, single-line strings — then a cross-surface pass: naming taxonomy, parameter vocabulary, tool overlap, instructions drift, length outliers. Produces grouped findings with file:line citations and a numbered options list. Use during polish, after a refactor, or before a release. Complements `field-test` (behavior testing) and `security-pass` (security audit).
Every string in a tool/resource/prompt definition is part of an LLM-facing API contract. The model reads the description, every parameter .describe(), the output schema, the recovery hints — and decides what to call and how. Definition language drifts: an internal mapping leaks into a parameter doc during a fix, a self-referential output description survives a refactor, a default that suited the developer at scaffold time stays after the typical call shape changes.
This skill is the review-time pass for that drift. Read each definition the way a mid-tier model with no project context would — can it pick the tool, fill the fields, and recover from errors using only the rendered schema?
Skill
Lens
design-mcp-server
Authoring rules at write-time
field-test
Behavior testing + a narrow 3-category leak audit
security-pass
Injection, scopes, input sinks
tool-defs-analysis (this)
LLM-facing language across the existing surface
field-test already audits descriptions for implementation leaks, meta-coaching, and consumer-aware phrasing during its catalog step — that's a fast shallow pass alongside live tool calls. This skill is the deeper review: 15 categories, every field, every recovery hint, every default value, with file:line citations — plus a cross-surface pass for the drift no single file shows.
Read-only. This skill produces a report; the maintainer applies fixes. While running it, do not run git, do not stage or commit, do not update the changelog, do not run devcheck, do not invoke wrapup or release workflows. Fixes flow through the normal authoring path (edit the definition, then re-run this skill if you want to verify).
When to Use
After a polish session or refactor that touched definitions
Before a release, alongside polish-docs-meta and security-pass
When the user says "review my tool definitions", "audit descriptions", "are my tool descriptions any good"
After scaffolding a new server but before it ships
Skip during initial authoring — add-tool and design-mcp-server cover that. Skip diff-only review — read each file in full so drift across the whole definition surfaces.
Inputs
Gather before starting. Ask if unclear:
Scope — whole server, specific definitions, or a single directory?
Severity floor — all findings (default), or skip nits?
Known concerns — anything the user already wants emphasized?
Steps
1. Build the inventory
find src/mcp-server/tools/definitions -type f -name "*tool.ts" 2>/dev/null | sort
find src/mcp-server/resources/definitions -type f -name "*resource.ts" 2>/dev/null | sort
find src/mcp-server/prompts/definitions -type f -name "*.prompt.ts" 2>/dev/null | sort
The *tool.ts / *resource.ts patterns also catch *.app-tool.ts / *.app-resource.ts. If the server's definitions live elsewhere (examples/, a packages workspace, …), audit those paths too. Also locate the server-level instructions string if the server sets one (the createApp option — grep -rn "instructions" src/ --include="*.ts"); it's audited in the cross-surface pass.
Use TaskCreate — one task per file. Mark each complete after its findings are captured.
2. Walk the 15 categories per file
Read each definition file in full. Apply every category — most files trip more than one. Capture each hit with file:line, the offending excerpt, and a one-line fix.
1. Voice & tense
Look in: tool / resource / prompt description.
Check: imperative present-tense. "Search for trials" beats "Searches for trials" or "This tool will search trials".
(Parameter .describe() text describes the value, not the tool — it doesn't need imperative voice.)
2. Internal leaks
Look in: every description and .describe().
Check: internal API routes, endpoint paths, API call counts, internal parameter mappings, sibling service names, version notes, TODOs.
Smell: "/api/v2/by-state", "Adds a second API call", "API requires two_year_period", "(deprecated; use bar_v2)", "TODO: support batch mode", "Used internally by FooService".
3. Audience leaks
Look in: every description and .describe().
Check: reader-naming or meta-coaching directed at the LLM rather than describing the tool.
Smell: "suitable for LLM consumption", "Treat the returned ID as the canonical Y", "Agents should…", "Callers should…", "When you call this tool…", any reference to "LLM", "agent", "Claude", "the model".
Field-test catches this in its leak audit; this skill is the more thorough pass.
4. Defaults
Look in: every .default(...) call in input schemas.
Check: the default matches the typical caller's case. A default that suited the developer at scaffold time often skews real calls — limit: 1 makes default-args searches useless, verbose: true floods context, dryRun: false on a destructive op invites an irreversible accident.
Smell: dev-convenience values that survived the schema's first draft, dangerous defaults on destructive operations, defaults that contradict the description's framing of typical use.
5. Recovery hints
Look in:errors: [{ recovery: '…' }] arrays, data.recovery.hint at throw sites in handler bodies.
Check: the hint directs the agent to its next action, not the developer to debugging. "Call pubmed_search with a narrower query" beats "Verify the configuration is correct" or "Internal error".
Smell: "Check the logs", "See documentation", "Contact admin", "Try again later" (with no condition), generic non-actionable text, hints that name internal classes or files.
6. Field descriptions
Look in: every field in input and output schemas; resource URI template variables.
Check: every field carries a .describe(), and it tells the agent what the value is — not just the field name restated, not silent on dynamic shapes. Enum variants — especially operation discriminators — are explained.
Smell:
An input field with no .describe() at all
name: z.string().describe('Name') — tautology
operation: z.enum([...]) whose variants are never explained
metadata: z.record(z.string(), z.unknown()).describe('Metadata') — opaque dynamic shape with no hint about keys/values
Optional fields with no note on when they're absent
Paging fields (total, hasMore, nextCursor) with semantics unstated — or a limit param that doesn't say whether it caps the page or the whole result
A URI template variable ({cid}) never described anywhere
Check: when one tool/resource is mentioned, when to reach for it is explained — and the references cover the relevant siblings, not a partial sample.
Smell: "Use foo_search to find IDs" (no when); a prompt naming 3 of 7 landscape-relevant tools; a tool description listing one sibling but not the others that fit the same workflow.
Check: optional upstream fields are acknowledged as such — not implied to always be present. format() doesn't print fabricated values for missing fields.
Smell:
pmid: z.string().describe('PubMed ID') when only ~60% of records have one (should be .optional() and noted)
format() printing **PMID:** undefined
A required field in output for an upstream value the API doesn't always return
Check: examples are domain-realistic — real-shaped IDs, real query strings, real values from the upstream domain. One example is usually enough.
Smell:.describe('Item ID (e.g., "abc123")') when real IDs have structure (NCT12345678); toy values ("foo", "bar"); padding multiple toy examples instead of one realistic one.
10. Structure
Look in: tool / resource / prompt description.
Check: single cohesive paragraph. No bullet lists, no blank-line-separated sections, no markdown headers inside the description.
Look in: mutator tools — any tool that writes, updates, deletes, appends, or patches (i.e., definitions without annotations.readOnlyHint: true).
Check:output carries a state-change discriminator (created, updated, mutated, unchanged) or before/after observable state the agent can use to confirm intent-effect match. The server reports what it observed; the agent decides whether it matches what it meant.
Smell: mutator output is { path, ok } or { success: true } — no pre/post state, no discriminator. Server-side defensive throws on synthetic deltas (file shrunk, count decreased) the server can't authoritatively classify as bugs.
12. Unit-bearing numeric names
Look in: every z.number() field in output schemas.
Check: the field name carries a unit when not pinned by context — sizeInBytes, durationInMs, priceInCents, latencyInMs. The .describe() drops in summarization or gets truncated; the field name persists into the JSON the agent reads. Scores, ratios, and percentages carry their range the same way — in the name or as the first thing in the describe (0–1).
Smell:size, duration, price, latency — bare names that force the agent to guess units; score/confidence with no stated range (0.87 and 87 both pass the schema). Exempt: index, position, page, offset, limit, totalCount, itemCount (dimensionless).
13. Constraints in validators
Look in: input schemas — every field whose .describe() states a format, range, length, or pattern.
Check: stated constraints are machine-enforced in the schema (.regex(), .min()/.max(), .int(), .length(), an enum) so they emit into the JSON Schema the client renders — a constraint living only in prose reaches a weaker model unreliably and burns retries on malformed input. Opaque-ID params also say how to obtain the value (which sibling tool returns it), not just its shape.
Smell:.describe('Date in YYYY-MM-DD format') on a bare z.string(); "max 100" in prose with no .max(100); an ID param whose describe gives the format but never the tool that produces it.
14. Annotations truthfulness
Look in: the annotations block on every tool.
Check: hints match what the handler actually does — clients gate confirmation prompts and retry policy on them. A purely-read tool carries readOnlyHint: true; deletes and overwrites aren't marked destructiveHint: false; retry-safe mutators carry idempotentHint: true; tools calling external services carry openWorldHint: true. If annotations.title is set, it still matches the tool's current name and behavior.
Smell:readOnlyHint: true on anything that writes; a read-only tool with no readOnlyHint (clients assume it can mutate); destructiveHint: false on a delete; a stale title surviving a rename.
15. Single-line strings
Look in: every description, .describe(), and error recovery / when string in a definition file.
Check: each is a single-line string literal. NEVER split one across lines with + concatenation ('part one ' + 'part two'), and never line-wrap a description into a \n-bearing template literal. The formatter does not break string literals, so a long single-line string passes formatting untouched — the line-width limit is not a reason to concatenate.
Why it's not cosmetic:+-concatenation forces every fragment to hand-carry its boundary whitespace, and a dropped trailing space silently fuses two words in the rendered schema the model reads ('…table_name. ' + 'Columns…' renders as table_name.Columns). Correct output is byte-identical to the single-line form, so the concatenation buys nothing and adds a class of silent contract corruption.
Smell: a string literal ending in ' + or " + at end of line; a description: value spanning multiple quoted fragments; a multi-line template literal inside a description (also a Structure finding, #10).
Fix: collapse to one single-line string literal.
3. Cross-surface pass
The per-file walk misses drift that only shows between files. After it, sweep the whole surface:
Naming taxonomy — verb prefixes mean one thing each across the surface (search_ / find_ / get_ / list_ / lookup_); the same verb carrying different semantics on different tools is a finding.
Parameter vocabulary — one name per concept everywhere: query vs q, limit vs maxResults, nctId vs nct_id on sibling tools is a finding.
Tool overlap — for any pair with adjacent scope, the two descriptions alone must answer "when X vs Y." If an agent can't pick, that's material.
Instructions drift — if the server sets instructions: every tool it names exists, workflow guidance reflects the current surface (new tools that belong in it, renamed or removed ones purged), and nothing contradicts a per-tool description.
Length outliers — a description several times longer than its siblings (attention drag), or a one-liner that underspecifies (selection risk).
Cross-surface findings use the same finding format, cited at the file:line you'd change (the instructions string is a citable location).
4. Report
Three sections.
Summary (1 paragraph)
Definitions reviewed, categories with findings, total finding count. One sentence on the single most material finding.
Findings
Group by category. Within each category, list each finding:
Excerpts are verbatim copy-paste from the file as read, line numbers from that read — re-verify any finding written from memory before it enters the report.
Two-level severity:
material — affects agent decisions (will mis-select tool, mis-fill input, mis-handle output, swallow an irrecoverable error)
nit — polish (style, voice consistency, minor phrasing)
Skip categories with no findings — don't list empty headers.
Options
Numbered, cherry-pickable. Map each item to a concrete change in a single file.
1. Tighten `metadata` description in `pubmed_fetch.tool.ts:42` — explain the dynamic shape (finding #3, material)
2. Drop bullet list from `clinicaltrials_get_field_definitions.tool.ts:18` description — single paragraph (finding #5, material)
3. Replace toy "abc123" example in `inventory_search.tool.ts:27` with real shape (finding #8, nit)
End with:
Pick by number (e.g. "do 1, 3, 5" or "expand on 2").
Checklist
Scope confirmed (whole server / module / specific files)
Severity floor applied — nits suppressed if user requested
Inventory built — every *.tool.ts, *.app-tool.ts, *.resource.ts, *.app-resource.ts, *.prompt.ts listed; server instructions located if set
Each file walked through all 15 categories (per-file, not 15 separate passes)
