codeitall
Server Details
Behavioral oracle for Rust crate APIs: runtime behavior, yank/advisory, deprecation, sig search
- Status
- Unhealthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- codeitalldev/codeitall
- GitHub Stars
- 0
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Tool Definition Quality
Average 4.5/5 across 8 of 8 tools scored.
Each tool has a distinct purpose, but `answer_api_question` acts as a router that internally dispatches to others, which may cause some boundary ambiguity. Agents might be unsure whether to use the router or the specific tool directly.
Names use snake_case but follow mixed patterns: some start with verbs (answer, compare, find, list) and others with nouns (behavior, package, pattern, signature). This inconsistency, while still readable, lacks a strict convention.
With 8 tools, the server covers its intended domain (Rust and TypeScript API knowledge) without being too sparse or bloated. Each tool serves a clear purpose within the scope.
The tool surface covers core workflows: free-text questions, specific lookups, comparisons, deprecation guidance, security checks, and consensus patterns. Minor gaps exist, such as missing a direct crate search by name, but the router tool and diagnostics compensate.
Available Tools
8 toolsanswer_api_questionAnswer Rust API questionARead-onlyInspect
Use this when an agent asks open-ended Rust API questions like 'how do I parse a duration string', 'which crate gives me HMAC verification', 'is this function deprecated', 'what's the current way to do JWT auth', or 'compare base64 crates'. Hand off free-text questions verbatim; the substrate routes them deterministically via rule-based intent detection (no LLM in the request path) and dispatches into signature_search, behavior_lookup, compare_implementations, or find_modern_equivalent as appropriate, falling back to bge-m3 cosine retrieval if no rule matches. Returns a structured verdict with routed_via, the primary recommendation, evidence, and caveats.
| Name | Required | Description | Default |
|---|---|---|---|
| language | No | rust | |
| question | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint=true annotation, the description discloses that routing is deterministic via rule-based intent detection with no LLM in the request path, falls back to bge-m3 cosine retrieval, and returns a structured verdict with routed_via, recommendation, evidence, and caveats.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately detailed and well-structured. It front-loads the usage guidance with examples, then explains the internal routing and return format without unnecessary verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (routing, multiple backends), the description covers all essential aspects: when to use, how it works, what it returns. No output schema exists, but the description explains the return structure adequately. Sibling tools are listed, aiding differentiation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains the 'question' parameter with examples and mentions the 'language' parameter has a default of 'rust'. While schema coverage is 0%, the description adds sufficient meaning for both parameters beyond the schema's type definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the purpose: answering open-ended Rust API questions. It provides concrete examples and explains the internal routing logic, distinguishing it from sibling tools like behavior_lookup and compare_implementations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use this when an agent asks open-ended Rust API questions', providing clear context. However, it doesn't explicitly state when not to use it or mention alternatives like signature_search, but the examples and routing description imply usage boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
behavior_lookupLook up runtime behaviorARead-onlyInspect
Use when the agent has a specific (crate, fn_name) pair and wants to know what inputs it actually accepts at runtime — e.g. (crate='jiff', fn_name='Timestamp::from_str') for methods, (crate='ascii85', fn_name='decode') for free functions. fn_name accepts BOTH qualified (Type::method / module::fn) and bare (method / fn) forms — the matcher tries the exact input first, then the alternate form; the matched_fn_name response field records the substitution when one happened. Returns the probe observation table verbatim from the substrate: each row is (input, outcome=ok|err|panic, value or error variant). Pass an optional inputs array to filter to specific input strings. On a zero-hit the response carries a diagnostics block (received_crate, received_fn_name, closest_crates, closest_fns_in_crate, hint) so the agent can self-correct without a dead-end round-trip. The substrate's discrimination findings live here — runtime behaviour the docs are silent or wrong about.
| Name | Required | Description | Default |
|---|---|---|---|
| crate | Yes | ||
| inputs | No | ||
| fn_name | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint annotation, the description adds extensive behavioral details: matching logic for fn_name forms, optional inputs filter, response format (probe observation table), and diagnostic block on zero-hit. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured, starting with the primary use case and then detailing behavior. Every sentence adds value without unnecessary verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema, the description comprehensively covers the return format (probe observation table with rows of input, outcome, value/error) and the diagnostics block, making it complete for the agent to understand expectations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description effectively explains each parameter: crate and fn_name are required, inputs is optional, and it details the matching behavior for fn_name. This compensates well for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: to look up runtime behavior for a specific (crate, fn_name) pair. It distinguishes itself from sibling tools like answer_api_question and compare_implementations by focusing on actual runtime acceptance of inputs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly states when to use: when the agent has a specific (crate, fn_name) pair and wants runtime behavior. It does not explicitly exclude alternative tools, but the context and sibling names provide implicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
compare_implementationsCompare implementationsARead-onlyInspect
Use when the agent asks about a task category — e.g. 'how do I parse JSON in rust', 'compare base64 crates', 'which datetime library handles RFC 3339 timezones right' — and wants the cross-implementation behavior table. The substrate returns side-by-side observations on the canonical input set: for each implementation (crate, fn_name), each input in the family's input set is paired with the observed (outcome, value_or_error_variant). Optional crates / fns arrays restrict the returned set; optional summary=true replaces per-input observations with an n_observations count for index-only listings (bounds response size by family-member count, not member × input count). Optional subfamily narrows to a registered sub-tag (e.g. task='base64', subfamily='base64' returns only canonical base64 crates, not ascii85 / base58 / hex / …) — call list_families to see available tags. Non-core family members (per a small hand-authored allowlist) carry an advisory caveat field warning that the entry was probed on shared inputs that may not reflect its typical usage. On a zero-hit family (n_attempted=0) the response carries a diagnostics block (received_family, available_families, closest_families, hint) so the agent can recover without guessing. Discrimination signal lives here — the docs-silent runtime behaviour pattern Guiding Principle #8 names.
| Name | Required | Description | Default |
|---|---|---|---|
| fns | No | ||
| task | Yes | ||
| crates | No | ||
| summary | No | ||
| language | No | rust | |
| subfamily | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true; description adds rich behavioral details: returns side-by-side observations, optional constraints, summary mode, subfamily filtering, caveat for non-core members, and diagnostics block for zero-hits. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is detailed but every sentence serves a purpose, front-loading the main use case. Slightly long but well-structured and informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 6 parameters, no output schema, and complex behavior, the description covers return structure, error diagnostics, and optional modes comprehensively. Completely adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description fully explains each parameter: 'crates'/'fns' restrict set, 'summary' replaces observations with count, 'subfamily' narrows, 'language' defaults to rust. Adds essential meaning beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool compares implementations for a task category, specifying the verb 'compare' and the resource 'implementations'. It distinguishes from siblings like 'list_families' by focusing on cross-implementation behavior tables.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use when the agent asks about a task category... and wants the cross-implementation behavior table.' It also directs to 'list_families' for available tags, providing clear context and alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
find_modern_equivalentFind modern equivalentARead-onlyInspect
Use when the agent suspects the code it's about to write uses a deprecated API — e.g. tokio::Runtime::new() (now tokio::runtime::Runtime::new()), chrono::DateTime::from_str (now feature-gated), or any path that points to a yanked or RustSec-advisory'd crate version. The substrate returns the current canonical alternative with a behavior summary if probed, and surfaces yank-status / advisory rows verbatim. For TypeScript/Next.js pass language='ts' with a deprecated Next.js API — e.g. next/router (now next/navigation), getServerSideProps (now an async Server Component), @next/font (now next/font), next/legacy/image (now next/image) — and the substrate returns the modern equivalent with the official upgrade-guide citation and how prevalent the deprecated vs modern form is across the live Next.js corpus.
| Name | Required | Description | Default |
|---|---|---|---|
| language | No | rust | |
| deprecated_call | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses behavioral traits beyond the readOnlyHint annotation: it returns a canonical alternative, behavior summary, yank-status/advisory rows, and prevalence data. This adds valuable context about the tool's outputs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is informative but somewhat verbose, with repetitive examples for TypeScript/Next.js. It is front-loaded with the core purpose, but could be more concise without losing clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the return values (canonical alternative, behavior summary, advisory rows) but does not specify the output structure or error handling. Given the tool's simplicity (2 params, no output schema), it is adequate but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description partially compensates by illustrating parameter values through examples (e.g., 'tokio::Runtime::new()' for deprecated_call, 'ts' for language). However, it lacks explicit formal parameter descriptions, leaving some ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to find the modern equivalent of a deprecated API. It provides specific examples for Rust (tokio, chrono) and TypeScript/Next.js, distinguishing it from sibling tools that focus on other aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly opens with 'Use when the agent suspects the code it's about to write uses a deprecated API', providing clear usage context. It includes concrete examples and mentions the language parameter, offering strong guidance on when to invoke the tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_familiesList probe familiesARead-onlyInspect
Use when the agent wants to enumerate the probe families codeitall has covered — typically before constructing a compare_implementations(task=…) call, or to recover from a typo flagged by compare_implementations's near-miss diagnostics. Returns the canonical family list with one-line descriptions, per-family counts, and the registered sub-family tags (Phase 1.12 W5; pass any as subfamily to compare_implementations): { families: [{ name, description, n_implementations, n_observations, sub_families }, …] }. The family names returned here are exactly the strings accepted by compare_implementations. No required parameters.
| Name | Required | Description | Default |
|---|---|---|---|
| language | No | rust |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, so description doesn't need to restate. It adds behavioral context: returns families array with specific fields (name, description, n_implementations, n_observations, sub_families) and states that family names are accepted by compare_implementations. However, it does not mention any rate limits or authentication, though for a read-only list tool this is acceptable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is informative and front-loaded with purpose and usage context. It includes a detailed output structure and relationship to sibling tool. Could be slightly more concise by omitting 'Phase 1.12 W5', but overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with one optional parameter and no output schema, the description covers most aspects: purpose, output structure, relationship to sibling, and usage context. The only gap is the missing parameter semantics, which slightly reduces completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% (language parameter has no description in schema), and the description does not mention the optional language parameter at all. Since the description needs to compensate for the schema gap, this is a significant omission. The agent may not know it can filter by language.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description explicitly states the tool enumerates probe families, specifies it returns canonical family list with descriptions, counts, and sub-family tags, and clarifies that family names are exactly the strings accepted by compare_implementations. This distinguishes it from sibling tools like compare_implementations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description provides explicit when-to-use guidance: typically before constructing a compare_implementations call or to recover from a typo flagged by near-miss diagnostics. It also notes 'No required parameters', giving clear context for invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
package_trustnpm package trust (advisories + maintenance)ARead-onlyInspect
Use before recommending or installing an npm package — to check whether it is safe and current. Pass the package name (and optionally a version, e.g. 4.17.0) with language='ts'. The substrate returns every OSV/GHSA security advisory affecting it (with severity, CVE aliases, the exact affected version range, and the advisory URL) and, if you pass a version, whether THAT version is affected; plus npm registry signals — whether the package is deprecated (and the maintainer's deprecation message), its latest version and last-publish date, and a maintained flag; plus how many real Next.js projects in the corpus depend on it and which declared version ranges the advisories actually bite. This is the cross-referenced security + maintenance signal a stale model cannot reliably know.
| Name | Required | Description | Default |
|---|---|---|---|
| package | Yes | ||
| version | No | ||
| language | No | ts |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite having no output schema, the description exhaustively lists what the tool returns: security advisories with severity, CVE, affected ranges, URLs; npm registry signals (deprecation, latest version, last-publish, maintained flag); dependency counts from real Next.js projects; and cross-referencing with declared version ranges. This far exceeds the readOnlyHint annotation by providing rich behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph of moderate length, front-loading the use case and then detailing outputs. It is efficient but could benefit from a more structured list for readability. Still, no wasted sentences.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters, no output schema, and moderate complexity, the description covers the inputs and outputs thoroughly. It explains the security advisory details, registry signals, and additional cross-referencing. It lacks error handling or non-existent package behavior, but overall it is complete enough for an AI agent to use effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It adds limited parameter context: 'Pass the package name (and optionally a version, e.g. `4.17.0`) with language='ts'.' This provides basic meaning but does not explain formats or constraints for version or language. It partially compensates but not fully.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Use before recommending or installing an npm package — to check whether it is safe and current.' It specifies a specific verb-resource combination and, given sibling tools are all in different domains (e.g., answer_api_question, behavior_lookup), it is well-differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says when to use the tool ('before recommending or installing an npm package') and provides parameter guidance (package name, version, language='ts'). It does not explicitly mention when not to use it or alternative tools, but the sibling context makes the usage clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pattern_consensusCross-project pattern consensusARead-onlyInspect
Use when the agent wants the cross-project CONSENSUS — 'how do most current Next.js projects actually do X?' — rather than a single example. Returns precomputed consensus records ('X% of projects matching predicate P do Y') for a category: auth-library (which auth library projects use), data-fetching-style (legacy page-level data fetching vs Server Actions vs Server Components), or routing-hooks (modern next/navigation vs legacy next/router). Each record carries its honest denominator N, the full outcome distribution with percentages, and the exact SQL predicate behind every number. Pass category to filter, or omit it to get all. Next.js corpus — pass language='ts'.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | ||
| language | No | ts |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the tool returns 'honest denominator N, the full outcome distribution with percentages, and the exact SQL predicate behind every number', going beyond the readOnlyHint annotation. No contradictions; the read-only nature is consistent with returning data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph of 4 sentences, each adding distinct value. It front-loads the usage scenario but could be slightly more structured (e.g., separate lines for categories and parameters). Still efficient and not verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given two optional parameters and no output schema, the description explains the return structure (records with denominator, distribution, SQL predicate) and lists the categories. It lacks details on error handling or invalid inputs, but for a simple tool, it is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description compensates by listing valid categories (auth-library, data-fetching-style, routing-hooks) and explaining that 'language' defaults to 'ts' for the Next.js corpus. It clarifies that omitting category returns all records, adding value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns 'precomputed consensus records' for categories like auth-library, data-fetching-style, and routing-hooks, distinguishing it from siblings that provide single examples or comparisons. It specifies the verb 'returns' and the resource 'cross-project consensus', making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly starts with 'Use when the agent wants the cross-project CONSENSUS... rather than a single example', providing a clear usage context. It mentions optional filtering by category and language but does not explicitly list when not to use or name alternatives, though sibling tools are available.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
signature_searchSearch by function signatureARead-onlyInspect
Use when the agent already knows the signature shape it wants — e.g. fn(&str) -> Result<Vec<u8>, _>, fn(&[u8]) -> Result<String, std::str::Utf8Error>, or impl Iterator<Item=Result<...>>. Returns ranked candidate functions with the crate they live in, downloads, yank status, and a compact behavior summary if probed. Pass the exact signature verbatim; the substrate normalises whitespace + identifiers on its end.
| Name | Required | Description | Default |
|---|---|---|---|
| shape | Yes | ||
| top_n | No | ||
| language | No | rust |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation indicates readOnlyHint=true, and the description adds that the tool returns ranked candidates with a behavior summary if probed, and normalizes whitespace/identifiers. No contradictions, and it provides useful context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, two sentences, front-loaded with purpose and usage instructions. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description mentions returned data (crate, downloads, yank status, behavior summary). It lacks detail on pagination or result limit behavior beyond the top_n parameter, but overall is adequate for a search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It explains the 'shape' parameter well ('pass the exact signature verbatim'), but does not describe 'top_n' (max results) or 'language' (default rust). This leaves ambiguity for these parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool searches by function signature and returns ranked candidate functions with details like crate, downloads, yank status, and behavior summary. It distinguishes from siblings by focusing on signature matching.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use ('when the agent already knows the signature shape it wants') and provides examples. Also instructs to pass the signature verbatim and explains normalization, which guides proper usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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