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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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
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?
The description discloses internal routing details (rule-based intent detection, no LLM, fallback to bge-m3 cosine retrieval) and the return structure (routed_via, primary recommendation, evidence, caveats). This adds significant value beyond the readOnlyHint annotation by explaining how the tool operates and what to expect.
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 efficiently conveys purpose, usage, and internal logic. It is front-loaded with examples and avoids unnecessary repetition. It could be slightly shorter, but it packs substantial value without being 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 the tool's complexity as a dispatcher, the description fully covers its behavior, routing, and output. No output schema exists, but the return structure is described. The annotations (readOnlyHint) are complemented, and the input schema's limitations are adequately addressed. The description leaves no critical gaps for an AI agent to misinterpret.
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 must compensate. It explains that the 'question' parameter should contain free-text questions verbatim, and the tool is for Rust, clarifying the 'language' default. However, it does not explicitly describe the 'language' parameter's possible values or behavior, so some detail is missing.
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 is for open-ended Rust API questions, giving concrete examples like 'how do I parse a duration string' and 'compare base64 crates'. It distinguishes itself from sibling tools by indicating it dispatches to them, making it clear this is a high-level query handler.
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 the tool ('when an agent asks open-ended Rust API questions'). It does not explicitly state when not to use it or suggest alternatives, but the context and sibling tools imply that specific queries might go directly to other tools. The guidance is clear enough for an AI agent.
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 readOnlyHint, description explains fn_name matching logic, verbatim probe data return, and zero-hit diagnostics. Adds behavioral context like self-correction hints.
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?
Front-loaded with usage condition, then explains parameters, response, and diagnostics. Each sentence adds value, though slightly longer than minimal.
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, but description fully portrays response format (probe table, diagnostics) and handles edge cases (zero-hit). Complete for using the tool 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?
With 0% schema coverage, description fully compensates: explains crate and fn_name with examples, optional inputs filter, and matching behavior. Every parameter gets meaningful explanation.
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?
Clearly states the tool looks up runtime behavior for a specific crate and function name, distinguishing it from sibling tools like answer_api_question or 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 tells when to use: when the agent has a specific (crate, fn_name) pair and wants runtime input acceptance. This provides clear context for usage.
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 include readOnlyHint=true, and the description confirms it is a read operation, returning observations without side effects. It describes the return format (observations, optional caveat for non-core members, diagnostics on zero-hit). There is no contradiction 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 a single dense paragraph that front-loads the main use case. While effective, it could be slightly more concise by splitting into shorter sentences. However, 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 absence of an output schema, the description fully explains the return values: side-by-side observations with outcome and error variant, optional caveat, diagnostics for zero-hit families. It covers all necessary information for the agent to use the tool correctly.
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%, but the description explains each parameter thoroughly. For example, it explains that 'summary=true' replaces per-input observations with a count, and 'subfamily' narrows to a registered sub-tag. It adds significant meaning beyond the schema 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 starts with 'Use when the agent asks about a task category... and wants the cross-implementation behavior table.' It clearly states the tool's purpose: to return a side-by-side comparison of implementations for a given task. It distinguishes from siblings like list_families by referencing it explicitly.
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 provides clear usage context: when the agent wants cross-implementation behavior for a task category. It explains optional parameters and when to use summary=true. It does not explicitly state when not to use this tool, but it does mention calling list_families for subfamily tags, implying 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?
Beyond the readOnlyHint annotation, the description details what the tool returns (canonical alternative, behavior summary, yank-status/advisory rows) for both Rust and TypeScript, adding significant behavioral context without contradiction.
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 relatively long but efficiently packed with examples and use cases, front-loading the purpose. It is structured with language-specific paragraphs, earning its length.
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 no output schema, the description fully explains the return values (canonical alternative, behavior summary, yank-status, advisory rows) and covers multiple languages, making it complete for the tool's complexity.
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 input schema has 0% description coverage, so the description must compensate. It gives example values for deprecated_call and mentions language='ts', but does not fully specify validation rules, formats, or all supported values (e.g., only Rust and TypeScript are implied). Adequate but not comprehensive.
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 finds modern equivalents for deprecated APIs, with specific examples for Rust and TypeScript, distinguishing it from sibling tools that cover other topics like API questions or package trust.
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 suspects the code... uses a deprecated API' and provides context with examples, but does not explicitly state when not to use it or mention alternatives among 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 provide readOnlyHint, and description adds behavioral details: return structure with descriptions, counts, sub-families, no required parameters.
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 sentence with good detail, well-structured, but slightly dense; could be split for readability without losing meaning.
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?
Covers usage context and return format in absence of output schema, but misses param documentation and could mention sibling alternatives explicitly.
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 has 1 optional parameter 'language' with 0% coverage; description fails to mention it or explain its effect, despite noting 'no required 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 the verb 'enumerate' and the resource 'probe families', and distinguishes from siblings like 'compare_implementations' by specifying usage context.
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 specifies when to use (before 'compare_implementations', to recover from typo) and implicitly names the alternative sibling tool.
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?
The description discloses comprehensive behavioral traits: it returns OSV/GHSA advisories with severity, CVE, version range, URL; npm registry signals like deprecation, latest version, last-publish date, maintained flag; and real project dependency data. This adds significant value beyond the readOnlyHint annotation, with 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 a single paragraph that front-loads the primary use case and then lists outputs. While slightly lengthy, every sentence adds value. Could be tightened but remains effective.
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 no nested objects, the description fully explains the output's contents in detail, including advisory details, registry signals, and dependency counts. It leaves no ambiguity about what the tool returns.
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%, but the description compensates by explaining each parameter: 'package' as npm package name, 'version' as optional version string (e.g., '4.17.0'), and 'language' defaulting to 'ts'. This adds meaningful context beyond the bare 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's purpose: 'Use before recommending or installing an npm package — to check whether it is safe and current.' It specifies the action (check safety/currency) and the resource (npm package), distinguishing it from siblings which cover other domains.
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'), providing clear context. It does not mention when not to use or name alternatives, but the sibling tools are sufficiently distinct that no exclusions are needed.
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?
Annotations already declare readOnlyHint=true, so main behavior is safe. Description adds details about output structure (denominator N, distribution, SQL predicate) and behavior when category is omitted, providing valuable 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?
Three sentences pack a lot of information with minimal waste. Front-loaded with usage guidance. Could be slightly more structured, but remains effective.
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 format (records with denominator, distribution, predicate). Covers parameters and categories. Could mention error handling for invalid categories or empty results, but largely sufficient for a read-only 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?
The schema has 0% coverage (no human-readable descriptions for parameters), but the description fully explains the 'category' parameter with specific enum values and behavior when omitted, and mentions the 'language' parameter with default. This compensates completely.
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 specifies the tool's purpose: returning cross-project consensus ('how do most current Next.js projects actually do X?') rather than a single example. It lists specific categories and distinguishes from siblings that return single examples or comparisons.
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 wants cross-project consensus') and contrasts with 'rather than a single example'. Provides category examples, but does not mention alternative sibling tools directly.
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?
Annotations indicate readOnlyHint: true, and the description adds value by detailing the returned information (ranked candidates, crate, downloads, yank status, behavior summary if probed). No contradiction; description supplements annotations well.
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 is information-dense without unnecessary words. It could be slightly more structured (e.g., separate sentences for usage and parameters), but overall it is concise and front-loaded.
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 with one required, no output schema, the description adequately covers the primary behavior and input format. However, it misses details on 'top_n' and 'language' parameters, and the output format is only partially described (ranked candidates, but not structure).
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% (no parameter descriptions in schema). The description compensates by explaining the 'shape' parameter with examples and instructions. However, 'top_n' and 'language' are not elaborated, though their schema constraints (min, max, default) are present.
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 searches by function signature, provides examples of signature shapes, and specifies the return information (ranked candidates, crate, downloads, yank status, behavior summary). It distinguishes from sibling tools that cover different search types (API questions, behavior lookup, etc.).
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 states 'Use when the agent already knows the signature shape it wants' and gives concrete examples. It advises passing the exact signature verbatim. It does not explicitly state when not to use it or mention alternatives, but the context is clear enough.
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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