Crc

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds that it is 'Keyless, offline' and returns unsigned value and hex, providing extra behavioral context without contradicting any 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 extremely concise, consisting of two sentences that front-load the purpose and provide essential details without any extraneous information.

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, single-parameter tool with no output schema, the description adequately explains what the tool does, its input format (UTF-8 text), and what it returns (unsigned value and hex). No further information is needed.

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?

Input schema has 100% coverage and parameter description. The description adds the constraint that the text must be UTF-8, which is not explicitly stated in the schema parameter description, thus adding meaning 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 computes the Adler-32 checksum of UTF-8 text and mentions the return format (unsigned value and hex). It references zlib, distinguishing it from similar checksum tools like CRC-32, but does not explicitly differentiate from siblings.

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 implies usage for offline, keyless checksum computation, but does not explicitly state when to use or not use this tool compared to alternatives like crc32. No when-not or usage context is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the tool is safe. The description adds important behavioral details: the default model is free, probing Anthropic requires the user's own API key (BYO key), and the return structure includes per-model score, confidence, signals, and raw_response plus a combined view. 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 concise at three sentences, front-loaded with the main purpose and key details. Every sentence adds value with no redundancy or fluff.

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?

Despite no output schema, the description fully explains the return format (per-model object with score, confidence, signals, raw_response plus a combined view). It covers all parameters, use cases, and key behaviors, making it complete for a simple probe 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 100%, so parameters are already well described. The description adds extra context: default model is 'Workers AI' and is free, '_apiKey' is optional and required only for Anthropic, 'context' helps disambiguate. This enhances understanding 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 action ('Probe one or more LLMs'), the resource (LLMs), and the outcome ('score visibility (0-100) per model'). It differentiates from sibling tools like 'ask_pipeworx' (which answers questions) and 'scan_competitor_ai_presence' (which scans competitor presence) by focusing on visibility scoring across multiple models.

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: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It explains when to use optional parameters (e.g., needing '_apiKey' for Anthropic). However, it does not explicitly mention when not to use it or compare directly to siblings, but the context is sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. Description adds contextual traits: routes to 4,770 tools, returns structured answers with citations, one fast call, and provides examples of authoritative data sources.

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 packed with valuable information but slightly verbose given the multiple examples and usage guidance. However, it is front-loaded with key preference and every sentence serves a purpose.

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 adequately explains return value (structured answer with citation URIs). All six parameters are well covered via aliases. Sibling differentiation is thorough, making the tool's role 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?

Schema coverage is 100% with clear alias documentation. Description adds value by explaining the parameter accepts natural language and providing example inputs, though schema already covers the semantics sufficiently.

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 handles factual questions with structured data, preferring over web search, and distinguishes from siblings ask_pipeworx_grounded and deep_research by specific use cases.

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 lists when to use (factual queries like 'what is', 'look up', etc.), when not to (step up for grounded or multi-part research), and names alternatives, providing comprehensive guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond annotations (readOnlyHint, etc.), the description details the same routing as ask_pipeworx, extraction using only tool results, and explicit refusal reasons, adding significant 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?

Single, well-structured paragraph front-loaded with purpose, then usage, return format, and failure modes. 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?

Covers purpose, sibling differentiation, usage trade-offs, return structure, failure modes, and cost. No output schema exists, so the description compensates fully.

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 100% with parameter descriptions; the description does not add meaning beyond the schema, meeting the baseline.

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 a hallucination-resistant answer mode for high-stakes reads, specifying it extracts answers only from tool results. It distinguishes itself from the sibling ask_pipeworx, which is for casual lookups.

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 advises when to use (answers to be quoted, cited, or acted on) and when to prefer ask_pipeworx (casual lookups), including cost trade-off (one extra LLM call).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate read-only, idempotent, non-destructive. The description adds extensive behavioral context: resolution process, fan-out logic, response shapes, safety checks for low confidence, closed markets, wide spreads, and cancellation rules. 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 comprehensive but verbose, spanning multiple paragraphs. It is well-structured with sections (classifiers, fan-out examples, response shapes, safety notes), but could be more concise without losing essential details.

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 (3 parameters, no output schema), the description covers all aspects: input types, processing logic, detailed output structure (market, analysis, evidence, resolver contract), edge cases (low confidence, closed markets, wide spreads), and safety mechanisms. It is fully self-contained.

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 100%. The description adds meaning beyond the schema: explains the market input formats (slug, URL, question text), depth options with defaults, and include_raw effects. It provides examples and clarifications for each parameter.

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: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies inputs (slug, URL, question text) and outputs (evidence packet + comparison), distinguishing it from sibling tools like arbitrage or edge trackers.

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?

Explicit use cases are provided: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also explains classifiers and fan-out patterns. However, it does not explicitly state when not to use the tool, though safety conditions are described.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly and idempotent. Description adds valuable behavioral details: data sources (SEC EDGAR for companies, FAERS for drugs), handling of off-calendar fiscal years, sorting by primary metric, and return of citation URIs.

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 dense single paragraph that packs extensive information efficiently. It is front-loaded with example phrases. Slight improvement possible with more structured formatting, but still very concise.

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 fully explains return data (paired data + citation URIs) and covers both entity types comprehensively, including data sources and sorting behavior.

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 100% for both parameters. Description enriches semantics by specifying tickers/CIKs for company, names for drug, and explaining the entity type meanings and data sources.

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 performs side-by-side comparisons of 2-5 companies or drugs in a single call, with example query phrases. It explicitly distinguishes from siblings by stating 'ALWAYS PREFER over sequential single-pack lookups'.

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 when-to-use triggers ('compare', 'vs', 'which is bigger', 'rank') and notes it replaces 8-15 sequential lookups. Lacks explicit when-not-to-use, but context implies single-entity queries should use entity_profile.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Adds useful context beyond annotations: returns unsigned 32-bit and hex, mentions non-cryptographic nature. No contradictions with annotations (readOnly, idempotent, etc.).

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?

Two concise sentences front-load the purpose and essential details 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 a single parameter and comprehensive annotations, the description fully covers what's needed: algorithm, input encoding, output format, and caveats.

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 single parameter 'text' is described in schema, but the description adds that it must be UTF-8 encoded, which the schema lacks, enhancing semantic understanding.

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 computes a CRC-32 checksum, specifying the algorithm (IEEE) and common use cases (zip/gzip/PNG), distinguishing it from siblings like adler32.

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 notes it's keyless and offline, so no special setup needed, and clarifies it's for error detection, not cryptographic security, guiding appropriate use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description adds critical context: account requirement, parallel decomposition, 15-60s latency, output format (findings packet with verbatim evidence, confidence, gaps[]), and limitations for certain topics. No 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 well-structured and front-loaded with critical info (account requirement). While slightly long, every sentence adds value. Minor room for trimming but overall 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 2 parameters, no output schema, and high complexity, the description is remarkably complete. It covers behavior, limitations, expected latency, output format, and comparison with siblings. Leaves no important gaps.

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 100%, and the description adds extra meaning: explains depth values (quick=3, standard=5, thorough=8) and that question can be broad/multi-part. This goes beyond the schema's own 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 clearly states the tool performs 'grounded multi-source research across Pipeworx's 1241 STRUCTURED data sources' and distinguishes it from open-web search. It uses specific verbs like 'decomposes', 'routes', and 'returns', and contrasts with sibling tools like ask_pipeworx.

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?

Explicit guidance on when to use (broad/multi-part questions over structured data) and when not to use (single lookups, breaking news), with specific alternative tool names provided. Also covers account requirements and paid plan restrictions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnly, idempotent, non-destructive. Description adds that it returns top-N tools with full schemas and curated examples, ready to call directly, no second lookup needed. Adds behavioral 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?

Description is a single paragraph, front-loaded with core purpose. Lists domains efficiently, explains return value, and provides usage guidance. Each sentence adds value, though slightly dense.

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 clearly states what is returned (top-N tools with names, descriptions, full schemas). Explains max limit and ready-to-call nature. Sufficient for a discovery tool given context signals.

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 100% and description explains the key 'query' parameter accepts natural language and lists aliases. Also provides default and max for 'limit'. Adds meaningful examples and alias behavior 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 it 'Find tools by describing the data or task' and lists specific domains (SEC filings, FDA drugs, etc.), distinguishing it from sibling tools that perform tasks rather than discover tools.

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 instructs 'Call this FIRST when you have many tools available' and hints when not needed with 'not just one answer'. Does not name specific alternative sibling tools, but context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds context about soft-failing patents (USPTO sunset), GDELT→GNews fallback, and lists data sources. No contradictions, but does not include rate limits or latency details that could further enhance transparency.

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 dense and front-loaded with examples, but it runs to three paragraphs with some redundancy (e.g., 'names not supported' mentioned twice). Still, every sentence adds useful information and the structure is logical.

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 exists, but description details return fields (cik, company_name, recent_filings with URIs, fundamentals, patents, news, LEI) and explains soft-fail and fallback behaviors. However, it lacks example output format and doesn't cover potential pagination or error handling, leaving minor gaps.

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 100%, but description adds value by clarifying that type must be 'company' and value can be ticker or zero-padded CIK, and explicitly notes names are not supported (directing to resolve_entity). This goes beyond the schema's enum and 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?

Description starts with clear examples of user intents and explicitly states the tool returns a 'full cross-source profile of a US public company'. It distinguishes itself from siblings like resolve_entity and compare_entities by listing specific use cases and data sources.

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?

States to 'ALWAYS PREFER over chaining single-pack... lookups when the user asks for a holistic view'. Provides explicit when-not-to-use: 'names not supported (use resolve_entity first)'. The guidance on when to use vs alternatives is clear and actionable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare destructiveHint=true and idempotentHint=true, so the description aligns without adding further behavioral details (e.g., behavior on missing key). It adds no contradictions but offers no extra context beyond the 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?

Two concise sentences: first states purpose, second gives usage guidance. Front-loaded and no unnecessary words.

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 simple schema (1 required parameter), full annotations, and no output schema, the description provides sufficient context for the tool's purpose and usage. Sibling tools are clear.

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 100% coverage with a description for the 'key' parameter. The description merely mentions 'by key' without adding new meaning, so baseline 3 is appropriate.

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 deletes a memory by key, using a specific verb ('Delete') and resource ('memory'). It distinguishes from siblings like 'remember' (store) and 'recall' (retrieve).

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: when context is stale, task done, or to clear sensitive data. It also recommends pairing with 'remember' and 'recall', providing clear context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses key behaviors: fetches the page, extracts title/description/key links, emits standard markdown. This adds value beyond annotations (readOnlyHint, idempotentHint, etc.) by detailing the extraction and output format.

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 three sentences, front-loaded with the main function, and every sentence adds value. No wasted words.

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 tool is simple with no output schema. The description explains the output ('single text blob ready to drop at site-root/llms.txt') and mentions target crawlers. It is nearly complete, though it could note error handling for invalid URLs.

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 100%, so the description does not need to add much. It does not elaborate on parameters beyond the schema, which is adequate. Baseline 3 is appropriate.

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: 'Generate a production-ready llms.txt file for any URL' and explains the process (fetches, extracts, emits). It distinguishes from unrelated sibling tools.

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 explicit use cases: 'getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.' It does not explicitly state when not to use, but the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds behavioral insight by listing the exact fields returned and clarifying that it returns active subscriptions by default, going 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?

Two sentences: first defines purpose and output, second provides usage guidance. No extraneous words; every sentence earns its place.

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 tool with one optional parameter and no output schema, the description is complete. It covers purpose, output fields, and usage context without gaps.

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 100%, with the single parameter include_inactive fully described in the schema. The description does not add additional parameter semantics, aligning with baseline for high coverage.

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 lists the caller's active subscriptions and enumerates the returned fields (id, type, etc.). It uses a specific verb 'list' and resource 'subscriptions', distinguishing it from sibling tools like subscribe and unsubscribe.

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 tells when to use the tool: to review what you're monitoring before adding more or to find an id to cancel. It doesn't mention when not to use, but the context is clear and helpful.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With all annotations false, description discloses write behavior, rate limiting (5/identifier/day), and that it's free with no quota consumption. No 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?

Single paragraph, front-loaded with purpose and usage. Every sentence adds value—no fluff.

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?

Without output schema, description covers what happens (digests, roadmap impact), constraints (rate limit, quota exemption), and behavioral notes.

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?

100% schema coverage, but description adds context: 'context' is optional, message should be 1-2 sentences, 2000 chars max, and enum values are re-explained clearly.

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?

Clear verb 'Tell' and resource 'Pipeworx team', specifies four categories of feedback. Distinct from sibling tools (e.g., compare_entities, scan_dependency) which serve different purposes.

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: for bugs, missing features/data, or praise. Also gives don't: 'don't paste the end-user's prompt.' Includes rate limit and quota info.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Adds context beyond annotations: self-aggregating, no PII, caching policy. Annotations already declare safe read, but description enriches with behavioral details.

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 with bullet points for use cases. No redundancy, front-loaded main purpose, 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?

For a 1-param read-only tool with no output schema, description explains return shape and caching. Fully sufficient for 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?

Schema covers 100% of parameters; description adds nuance: shorter windows for hot topics, longer for steady-state. Adds 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?

Description clearly states it returns top tools, top packs, and total call volume over a window. Verb 'returns' with specific resource objects. Distinct from siblings like 'discover_tools'.

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 lists three use cases (discovering hot data sources, confirming canonical tool, checking alignment). No explicit when-not-to-use, but context is clear and sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Despite annotations indicating readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, the description adds extensive behavioral context: monotonicity checks, partition checks with sum-of-YES prices, similarity threshold (0.30 Jaccard), placeholder filtering (20% threshold), fill check against CLOB depth with realizable_edge_pp <= 0 meaning no trade. No contradiction with annotations; readOnlyHint is consistent as no state mutation occurs.

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 well-structured. It front-loads the core purpose and then organizes information by mode, response structure, and additional checks. Every sentence adds value, though some sections could be slightly more compact. Overall, it earns 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?

Given the complexity of the tool (arbitrage detection with multiple modes, fill checks) and the absence of an output schema, the description fully explains the response fields (opportunities[], partition_check, suggested_trade, gap_pp, etc.) and the fill check logic. It also covers edge cases like skipped_low_similarity and placeholder filtering. The mention of polymarket_fill_risk for custom sizing completes the context.

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 100% with both parameters having descriptions. The tool description adds significant value by explaining the modes (event vs topic), what an event slug looks like (examples given), and what a seed question is. It also details the internal logic for each mode, enriching the agent's understanding beyond the basic 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 that the tool finds arbitrage opportunities on Polymarket using monotonicity violations and partition-sum checks. It distinguishes three modes: trending scan (no args), event mode (single market), and topic mode (cross-event scan). The verb 'find' and resource 'arbitrage opportunities' are specific and differentiate from siblings.

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 explicit instructions on when to use each parameter: 'Call with NO args for a trending_scan', 'pass event for the strongest per-event partition_check', and 'topic for a themed cross-event scan'. It recommends event mode for a specific market and explains the cross-event use case. It also references polymarket_fill_risk for custom sizing, but does not explicitly state when not to use this tool, which is a minor gap.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark the tool as readOnly, idempotent, and non-destructive. The description adds significant behavioral context: caching (1h at KV level), response structure with diagnostics, and edge metric details. 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 very long and dense with technical details, making it less concise. However, it is well-structured with clear sections (e.g., FIVE MODEL FAMILIES, TRADEABLE-EDGE KNOBS, RESPONSE TOP-LEVEL) that aid readability despite the 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?

Given the absence of an output schema, the description thoroughly explains the return format (by_segment, diagnostics), edge cases (Fed bets, empty segments), and caching behavior. It provides sufficient information for an agent to understand and invoke 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?

All 9 parameters are fully described in the input schema (100% coverage). The main description provides additional context by explaining how knobs like min_partition_leg_kelly interact with the model logic, adding value beyond 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 clearly states the tool's purpose: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It is specific with verb 'scan' and resource 'Polymarket markets', and distinguished from siblings like polymarket_arbitrage by focusing on Pipeworx disagreement.

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 'Built for "what should I bet on today"' and explains tradeable-edge knobs like min_liquidity and max_spread_pp. It lacks explicit mention of when not to use or direct sibling comparisons, but the context is clear enough for an agent to infer appropriate use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds valuable context: data from daily snapshots with gaps meaning no scan, decay from daily closes not intraday, and a 60-day snapshot TTL. 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 approximately 120 words, well-structured with core question upfront, then response fields, then limits. Slightly verbose but every sentence adds value. No wasted words.

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 provides a thorough breakdown of response fields including edge_pp_net time-series, trend labels, decay_pp_per_day, expired opportunities with lifespan_days, and snapshot dates. Limits are clearly stated. Minor missing aspects: no example or explicit data type notes, but nearly 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?

Schema coverage is 100%, so baseline is 3. The description adds default values, clamping (days), and response structure details (tracked[], expired[], snapshot_dates[]) that go beyond the schema's property 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 clearly states the tool provides 'edge persistence and decay telemetry' and answers 'how long has this edge existed and is it shrinking?' It uses a specific verb ('answers') and resource ('edge persistence'), and distinguishes from sibling 'polymarket_edges' by focusing on historical trends rather than current edges.

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 implies when to use (to differentiate a fresh wide edge from a 3-week-old one) and includes LIMITS about history depth and decay computation. However, it lacks explicit 'when not to use' or direct alternatives, leaving some inference to the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds substantial behavioral context: walks the ladder, returns specific fields (top_of_book, vwap_fill_price, slippage_pp, etc.), and explains that partial basket fills convert arbitrage into unhedged directional positions. 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?

Description is dense but front-loaded with key purpose and requirements. Uses capitalization for mode labels (SINGLE-MARKET, BASKET) to aid scanning. While somewhat lengthy, each sentence adds specific behavioral or usage information. Could be slightly tighter but efficient overall.

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 complexity (two modes, 4 parameters, no output schema), description covers all necessary aspects: required parameters, mode behavior, return fields for each mode, risk warnings, and usage context. Completely informs an agent when and how to invoke the 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 100% with descriptions for all 4 parameters. Description adds value by interpreting size_usd for basket mode (settlement notional, shares per leg), explaining side defaults (auto for basket and per mode), and clarifying market vs event modes. Baseline 3 is raised because description provides meaningful extra context 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?

Description clearly states it performs a realizable-vs-theoretical edge check against live order book depth. It distinguishes single-market and basket modes, and contrasts with sibling tools like polymarket_arbitrage and polymarket_edges by specifying it's a pre-trade risk check.

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 explicitly says 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500' and warns about partial basket fills. Provides clear context but does not explicitly state when not to use alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds extensive behavioral details beyond annotations: two modes, safety field conditions (compatibility_warning cases), temporal alignment, skipped cross counters, and the caveat about rarity of real spreads. This fully discloses behavioral traits and matches the readOnlyHint annotation.

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 with clear sections but is verbose. Every sentence adds value, but it could be slightly more concise. The front-loading of purpose and modes is good.

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 complexity and no output schema, the description fully explains return values (leg prices, spreads, safety fields) and covers all aspects of the tool's behavior. It leaves no critical gaps for effective usage.

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 100% schema coverage, the description adds meaning by explaining the two modes' parameter usage, providing examples, and describing how explicit parameters override topic. The description clarifies the roles and combinations 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 computes cross-venue spread between Kalshi and Polymarket with specific modes. However, it does not explicitly distinguish itself from sibling tools like polymarket_arbitrage or polymarket_edges, which might target similar concepts on single venues.

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 explicit usage context: two modes, explanation of safety fields like compatibility_warning, and a caution that pre-mapped topics often return warnings. It doesn't directly name alternatives but implies when not to use (e.g., incompatible bet shapes).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds context about scoping ('scoped to your identifier') and re-derivation avoidance, enhancing transparency 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, no fluff. Key information is front-loaded: what it does, how to use, and related tools. Every sentence earns its place.

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 simple tool with one optional parameter and no output schema, the description covers retrieval, listing, scoping, and pairing with siblings. No gaps.

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 100% schema coverage, the description adds value by explaining that omitting 'key' lists all keys, which is not in the schema description. This helps the agent understand parameter optionality.

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 states a specific verb ('Retrieve') and resource ('a value previously saved via remember'), and distinguishes from sibling tools by explicitly mentioning 'remember' and 'forget'. It also clarifies the listing behavior when key is omitted.

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 explains when to use the tool ('to look up context the agent stored earlier') and mentions pairing with 'remember' and 'forget'. It also notes the omitting key lists all keys. It lacks explicit 'when not to use', but context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds valuable context: return fields (source, citation_uri, raw payload), mark_read side effect, and polling suitability. 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 front-loaded with the main purpose and structured logically. Slightly verbose but every sentence adds value; could be trimmed slightly for conciseness.

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?

Despite no output schema, the description explains key return fields and behavior. Covers all essential aspects for a read-only feed tool with five optional parameters, including polling and alternative access.

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 covers all parameters 100%. Description adds real-world examples (e.g., 'sec_8k' for type, ISO timestamp for since) and explains mark_read's effect on future calls, providing meaningful enrichment.

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 pulls fired events from the subscription feed and returns recent alerts. It uses specific verbs and resources, and the purpose is easily distinguishable from sibling tools like subscribe or recall.

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 guidance on filtering by type and since, mark_read behavior, and mentions polling works fine. Also gives an alternative access URL. However, does not explicitly contrast with sibling tools like list_subscriptions or when not to use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations cover safety/idempotency. Description adds fan-out details, fallback (GDELT→GNews), soft-fail on USPTO, and return structure including citation URIs. Exceeds what annotations provide.

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?

Dense single paragraph, front-loaded with examples. All sentences earn their place, though slightly less structured than ideal.

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 3 required params and no output schema, description covers sources, fallback, parameter formats, and return structure thoroughly. No gaps for agent to guess.

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 100%, but description adds rich detail: examples for 'since' (ISO vs relative), explains 'type' is only company, and clarifies 'value' accepts ticker or CIK. Elevates understanding 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?

Description starts with concrete query examples ('What's new with X', 'latest on Y') and explicitly states it is a change feed for a company, fans out to multiple sources, and distinguishes from sibling tool entity_profile for static data.

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 clear query patterns and recommends entity_profile for static profiles. Lacks explicit 'when not to use' but context on alternatives is strong.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Adds substantial behavioral context beyond annotations: scope by identifier, persistence for authenticated vs anonymous (24h). No contradiction with annotations (idempotentHint=true, destructiveHint=false).

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, front-loaded with purpose, no redundant words. Every sentence earns its place.

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 write tool with two string params and no output schema, the description covers persistence behavior, scoping, and pairing with recall/forget. 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?

Schema coverage is 100% for both params. Description adds naming conventions (e.g., 'subject_property') and usage examples, enriching 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 saves data for later reuse, with specific examples (ticker, address, preference) and distinguishes from sibling tools recall and forget.

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 you discover something worth carrying forward'. Mentions pairing with recall/forget and scoping by identifier. Lacks explicit when-not-to-use but context is sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds that it cascades through several lookup endpoints internally and auto-disambiguates, providing rich behavioral 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 well-structured with examples and bullet points, but could be slightly more concise. However, it earns its length by providing necessary detail.

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 return formats for both types (ticker, CIK, company_name, citation for company; RxCUI, ingredient, brand, citation for drug) and mentions auto-disambiguation and internal cascade, making it fully informative.

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 100%, but description adds significant meaning for the 'value' parameter by specifying accepted formats for each type (ticker, CIK, name for company; brand/generic for drug). This enhances understanding beyond the schema's generic 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 resolves user-spoken names to canonical identifiers, gives concrete examples for each type, and explicitly says 'Use FIRST whenever you have a name but need an ID.' It distinguishes itself from siblings by specifying it replaces 2-3 manual lookups and is the primary tool for name-to-ID conversion.

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?

It explicitly states 'Use FIRST whenever you have a name but need an ID,' and details supported types (company, drug) with examples. While it doesn't explicitly list when not to use, the directive is strong and context makes it clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, which fully cover the safety profile. The description adds process details (probes with ai_visibility_check, ranks results) but does not expand beyond the annotation hints. No 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 three well-structured sentences: first states the action, second explains the process, third gives a concrete use case and return format. No fluff, and the key information is 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 no output schema, the description mentions the return format (ranked list with score, confidence, signal density) and explains the probing process. It omits edge cases like insufficient entities (<2) but is otherwise complete for a read-only comparison tool with rich annotations.

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 100%, so the schema already documents all four parameters. The description does not add new semantic meaning beyond the schema; it only rephrases the tool's action. Baseline 3 is appropriate.

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 AI visibility across multiple entities side-by-side, using a specific verb ('Compare') and resource ('AI visibility'). It distinguishes from sibling 'ai_visibility_check' (single entity) and 'compare_entities' (generic comparison) by specifying the AI visibility domain and batch comparison.

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 a concrete use case ('competitive AI-marketing audits') and implicitly distinguishes from the single-entity 'ai_visibility_check' by emphasizing side-by-side comparison. However, it does not explicitly state when not to use or list alternative tools by name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint, openWorldHint, idempotentHint. Description adds key behaviors: composite fan-out, graceful degradation on partial failures, and first-measurement timing (5-30s) for bundlephobia.

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?

Description is dense but well-structured, front-loading the core purpose. Could be slightly shorter by omitting explicit sibling differentiation (already covered in guidelines), but still 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?

Given no output schema, the description thoroughly explains return structure (summary, advisories, links, alternatives) and edge cases (partial failures, timing). Fully equips the agent.

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 100% with descriptions, but the description adds value by noting scoped packages are accepted and version defaults to latest, aiding parameter usage.

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 composite check combining deps.dev and bundlephobia for npm packages. It specifies the verb (scan), resource (dependencies), and scope (npm only), distinguishing it from siblings like validate_claim.

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 agent asks 'is X safe/popular/small' or 'what does adding lodash cost me'. Also notes ecosystem limitations (npm only v1) and directs other ecosystems to deps.dev:version directly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, etc. The description adds valuable behavioral details: embedding model, window size, truncation flag, and offset feature for verification. 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 concise and well-structured: first sentence defines purpose, then examples, pairing, and technical details. 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?

Despite no output schema, the description explains return structure (passages with offsets and scores) and mentions truncation flag. Covers all aspects needed for an agent to use and interpret results 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?

Input schema has 100% coverage with descriptions for all 3 parameters. The description reinforces these by providing examples (e.g., natural-language query examples) and operational context (max chars for text), 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 it performs semantic search inside a fetched record, using specific examples (SEC 10-K, article) to illustrate usage. It distinguishes from sibling tools like ask_pipeworx_grounded by explaining the pairing strategy.

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 when to use ('record too big to cram into prompt') and provides an alternative (ask_pipeworx_grounded). Also mentions the char cap and truncation behavior, guiding when not to use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond annotations (readOnlyHint=false, idempotentHint=true, etc.), the description adds significant behavioral context: requires OAuth account, type-specific parameter details, delivery channel behaviors (webhook signing secret returned once, auto-disable after 10 failures, SMS cap, phone verification). This fully informs the agent of side effects and constraints.

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 somewhat long but well-organized: starts with purpose, then lists types with examples, and finishes with delivery options. Every sentence adds value. It could be slightly more concise with bullet points, but the structure is logical 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 complexity (3 parameters, nested objects, no output schema), the description is remarkably complete. It explains return value (subscription ID), type-specific parameters, all delivery channels with usage and limitations, prerequisites, and how to retrieve alerts. No obvious gaps.

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 100% description coverage, and the description expands on each parameter with concrete examples (e.g., sec_8k with items, polymarket_edge with topic, fred_series with series_id). It also adds constraints like phone must match verified account, webhook must be HTTPS, and provides signing details. This adds considerable meaning 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 creates a proactive monitoring subscription to a live-data event stream and returns the new subscription ID. It lists supported types and delivery channels, making the purpose unambiguous. It distinguishes from siblings by focusing on creation rather than listing or reading alerts.

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 prerequisites (Pipeworx OAuth account) and explains delivery channels including how to retrieve alerts after subscribing (via recent_alerts or GET URL). It gives type-specific usage examples and constraints (SMS cap, phone verification, webhook auto-disable). It does not explicitly mention when not to use this tool or name alternative tools, but the context is strong enough for typical use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context beyond annotations by specifying that the tool returns 'category-bucketed example questions' drawn from a 'live catalog of thousands of tools' and that each example includes the exact tool+argument shape. This enriches the agent's understanding of 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 somewhat long but well-structured: it front-loads the essential purpose with natural language queries, then details the return structure and usage guidance. Every sentence serves a purpose, though a few words could be trimmed (e.g., the list of categories is extensive but helpful). Overall, it is appropriately detailed for an onboarding tool.

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 role as an onboarding entry point, the description covers all necessary aspects: what it does, when to use it (first), how to call it (with/without topic), what it returns (category-bucketed examples with tool+argument shapes), and its relationship to meta-tools. No output schema exists, but the description sufficiently explains the return value. Sibling tools provide further context. It is complete for this use case.

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 100% with one optional parameter (topic) having a description. The description adds meaning beyond the schema by explaining that omitting topic gives a 'full spread' and passing a specific topic (e.g., 'finance', 'pharma') focuses the results. This provides practical usage context not available from the schema alone.

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 begins with multiple natural language queries that directly map to the tool's purpose (e.g., 'What can I ask Pipeworx?', 'give me ideas'). It clearly states it is the onboarding entry point that returns category-bucketed example questions with exact tool and argument shapes. This distinguishes it clearly from siblings like ask_pipeworx (which answers questions) and discover_tools (which lists tools).

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 this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools [...]'. It also explains when to pass the optional topic parameter and when to omit it for a full spread. This provides strong guidance on when and how to use this tool vs alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Description adds ownership enforcement, deactivation behavior, and impact on historical events beyond the annotations, which only indicate non-destructive and idempotent 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?

Two sentences, front-loaded with action and resource, every sentence adds value, no 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?

Complete for a simple tool: covers ownership, side effects, and relation to historical data. No output schema needed.

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?

Single parameter 'id' with schema coverage 100%. Description adds context that the id is returned by subscribe, linking tools.

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 'Cancel a subscription by id', specifying verb and resource. Distinct from siblings like 'subscribe' and 'list_subscriptions'.

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 mentions ownership enforcement and that the row is deactivated, not deleted. Provides clear context but does not name specific alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds details about the return values (verdict, structured form, actual value, citation, delta) and that it replaces multiple calls. No 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 front-loaded with natural-language patterns and well-structured, but slightly verbose. It efficiently conveys purpose and usage without unnecessary words.

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 a single parameter and no output schema, the description covers the domain, return types, and typical use cases adequately. It provides enough context for an agent to select and invoke the 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?

With 100% schema description coverage, baseline is 3. The description adds value by specifying supported claim types (company-financial) and providing examples, going beyond the schema's 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's for natural-language claim verification against authoritative sources, specifically company-financial claims via SEC EDGAR + XBRL. It also explains it replaces 4-6 sequential calls, differentiating it from sibling tools.

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 whenever the agent needs to check whether something a user said is factually correct.' It also limits the domain to company-financial claims. While it doesn't explicitly state when not to use it, the domain restriction is clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.