codeitall
Server Details
Behavioral oracle for Rust crate APIs: runtime behavior, yank/advisory, deprecation, sig search
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- codeitalldev/codeitall
- GitHub Stars
- 0
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Tool Definition Quality
Average 4/5 across 6 of 6 tools scored.
Each tool serves a distinct purpose: answer_api_question is a high-level router, behavior_lookup probes specific function inputs, compare_implementations compares multiple crates, find_modern_equivalent finds replacements for deprecated APIs, list_families enumerates probe families, and signature_search searches by signature. No two tools have overlapping functionality.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., answer_api_question, behavior_lookup, compare_implementations, find_modern_equivalent, list_families, signature_search). The naming is predictable and readable.
Six tools is well-scoped for a Rust API exploration server. Each tool covers a key aspect: routing, behavior probing, cross-comparison, deprecation handling, family listing, and signature search. No unnecessary tools, and the surface is focused.
The tool set provides a complete workflow for Rust API questions: ask a question (answer_api_question), probe specific behaviors (behavior_lookup), compare implementations (compare_implementations), find modern equivalents (find_modern_equivalent), list available families (list_families), and search by signature (signature_search). No obvious gaps.
Available Tools
6 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?
No annotations are provided, so the description carries the full burden. It describes the routing mechanism (rule-based, no LLM), fallback to bge-m3 retrieval, and the structured verdict format. This is sufficient for an agent to understand internal behavior.
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 that front-loads the purpose and provides necessary details about routing. While it is slightly verbose, it contains no wasted sentences and every sentence adds value.
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 to multiple sub-tools) and lack of an output schema, the description fully explains the input expectation, routing logic, fallback mechanism, and return format. An agent has enough context to decide when to call and what to expect.
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%, meaning the schema itself lacks parameter descriptions. The tool description does not mention or explain the two parameters ('language' and 'question') beyond their existence. This is a critical gap as the agent cannot infer correct parameter usage from the description.
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 it is for answering open-ended Rust API questions, provides multiple example queries, and explains the internal routing logic. It distinguishes itself from sibling tools by explicitly listing them as the targets of the routing.
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 'Use this when an agent asks open-ended Rust API questions' and gives concrete examples. However, it does not provide explicit guidance on when NOT to use it or how it compares to directly using the sibling tools.
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?
With no annotations, the description fully discloses the tool's behavior: it returns a probe observation table with rows showing input, outcome (ok/err/panic), and value/error variant. Mentions that it surfaces discrimination findings undocumented elsewhere, adding valuable 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?
Concise paragraph with no redundant sentences. Front-loaded with usage guidance, then returns format, then optional filtering. Every sentence contributes unique value.
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 explains the return structure well. Covers error outcomes (err, panic) but doesn't mention potential limitations or edge cases. Still, sufficiently complete for most use cases.
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?
Despite 0% schema coverage, the description thoroughly explains all three parameters: crate and fn_name as required (with example), and optional inputs array for filtering. Adds meaning beyond the raw schema types.
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?
Describes a specific verb-resource combination: looking up runtime behavior for a (crate, fn_name) pair. Distinguishes clearly from sibling tools like signature_search or compare_implementations by focusing on runtime probe results.
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 has a specific (crate, fn_name) pair'), providing clear context. Lacks explicit when-not-to-use or alternative tool references, but the usage scenario is well-defined.
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?
Describes the general output format and that it uses 'canonical input set' and 'Discrimination signal', but lacks details on side effects, data sources, or operational semantics. With no annotations, more detail is needed.
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 verbose paragraph with jargon like 'Guiding Principle #8' and 'Discrimination signal', making it less concise and harder to parse.
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?
Explains the output format but leaves ambiguity about the 'family's input set' and how implementations are selected. With no output schema, more detail is needed for an agent to use it autonomously.
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 schema has two parameters (task, language) with 0% description coverage. The description adds minimal guidance—only examples for 'task' and implies Rust from context; language is not explained.
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 given task category, providing cross-implementation behavior tables. It distinguishes from siblings like behavior_lookup and signature_search by its comparative focus.
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?
Provides explicit examples (e.g., 'compare base64 crates') and when to use, but does not explicitly state when not to use or mention alternatives, leaving some room for interpretation.
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.
| 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?
With no annotations, description carries full burden. It explains that the tool returns canonical alternatives, behavior summary, yank-status, and advisory rows. This is comprehensive for a search tool.
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?
Single paragraph includes usage context, examples, and behavior. Could be structured into separate sentences for clarity, but length is appropriate.
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?
No output schema means description must cover returns, which it does partially. Lacks explicit parameter documentation and error conditions. Adequate for a simple lookup 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%, but description only illustrates deprecated_call through examples, not explicit format or constraints. Language parameter has default but no documentation. Description does not compensate adequately.
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 clearly states it finds modern equivalents for deprecated API calls, with specific examples like tokio and chrono. It distinguishes from sibling tools by focusing on deprecation modernization.
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?
Opens with 'Use when the agent suspects the code it's about to write uses a deprecated API', providing explicit context. Does not list alternatives but context implies distinct use case from siblings.
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.
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?
No annotations are provided, so the description carries the full burden. It discloses the return type (ranked candidates with metadata), mentions normalization of input, and notes a 'compact behavior summary if probed'. However, it does not discuss auth requirements, rate limits, or potential side effects. The description adds moderate behavioral context beyond what is implicit.
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 with concise sentences. It front-loads the key use case and examples. There is minimal redundancy, though it could be slightly more structured (e.g., bullet points for parameters). 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?
With 3 parameters, no output schema, and no annotations, the description covers the main use case and return format but leaves gaps: it does not explain the two optional parameters or the full shape of the return value. It is functional but not comprehensive.
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%, so the description must compensate. It explains the 'shape' parameter with examples and the verbatim requirement, but does not describe 'top_n' or 'language' beyond what the schema provides (min, max, default). This partial coverage justifies a score of 3.
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 search for function signatures when the agent knows the shape. It provides concrete examples (e.g., `fn(&str) -> Result<Vec<u8>, _>`) and specifies what is returned (ranked candidates, crate, downloads, yank status). This clearly distinguishes it from sibling tools like `answer_api_question` or `find_modern_equivalent`.
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 'Use when the agent already knows the signature shape it wants' and gives usage examples. While it doesn't provide exclusions or explicitly compare with siblings, the context is clear. A higher score would require explicit 'when not to use' or alternative tool references.
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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