Pypi

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

Annotations already declare readOnly, idempotent, non-destructive. Description adds context: default model is free, Anthropic requires BYO key and direct payment, and returns per-model scores with raw responses. 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?

Description is concise at 3-4 sentences, front-loaded with main purpose, and each sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, description explains return format (per-model with fields + combined view). Covers main functionality and parameter usage. Could be more detailed on combined view structure, but adequate for understanding.

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%, baseline is 3. Description adds meaning beyond schema: explains models defaults, _apiKey necessity, and context disambiguation. Provides clear usage instructions for 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?

Description clearly states the tool probes LLMs for knowledge about entities and scores visibility. It specifies the verb 'probe' and resource, and outlines use cases like AI-marketing audits, but does not explicitly distinguish it from sibling tools like scan_competitor_ai_presence.

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 mentions default model and optional Anthropic key, and suggests use cases, but does not provide explicit guidance on when to use this tool versus alternatives (e.g., siblings like scan_competitor_ai_presence). Usage is implied but not fully clarified.

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, and destructiveHint. The description adds context about routing to 3,745 tools, 884 sources, and citation URIs, which goes beyond annotations. It does not cover potential rate limits or authentication, but the annotations already address safety, so the description adds meaningful 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 front-loaded with the key usage guidance ('PREFER OVER WEB SEARCH') and is well-organized with examples. While it is relatively long, each sentence contributes meaningfully, and there is no redundancy. Could be slightly more concise, but it's 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's complexity (routing to many sources) and the absence of an output schema, the description provides sufficient context about the return format (structured answer with citations) and the scope of data. It covers the parameter adequately and addresses common use cases, making it complete for an AI agent to use effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with multiple aliases for the 'question' parameter all documented. The description reinforces that the parameter accepts natural language questions and provides examples. It adds value by guiding the AI on how to phrase queries, but the schema already covers the basics, resulting in a score of 4.

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 routes factual questions to authoritative sources across 3,745 tools and returns structured answers with citations. It specifies the domain (SEC filings, FDA drugs, etc.) and distinguishes itself from web search, making its purpose explicit and differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'PREFER OVER WEB SEARCH' and lists specific use cases (e.g., 'current US unemployment rate', 'Apple's latest 10-K'). It provides examples and tells the AI when to invoke this tool over alternatives, including trigger phrases like 'what is', 'look up', etc.

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, destructiveHint. Description adds behavioral details: extraction is limited to tool result, and explicit refusal reasons (not_in_source, no_tool_match, etc.) are returned. 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?

Description is a single concise paragraph with 4-5 sentences. Core purpose is front-loaded. Information dense but not verbose. Minor structure improvement possible (e.g., list refusal reasons) but not necessary.

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 all aspects: purpose, usage context, return format, refusal reasons, cost, and differentiation from sibling. Despite no output schema, the description defines return structure and refusal types explicitly, making it complete for an 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 all parameters being aliases for 'question'. Description adds no further meaning beyond what schema already says, simply repeating 'Your question in natural language'. Baseline score of 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 is a 'hallucination-resistant answer mode for high-stakes reads' and explains the extraction process. It distinguishes itself from the sibling tool ask_pipeworx by emphasizing the grounded answer extraction and explicit refusal mechanism.

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 answer will be quoted, cited, or acted on') and when not to ('prefer ask_pipeworx for casual lookups'). Provides concrete examples like financial verdicts and legal claims. Also mentions the extra cost trade-off.

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 goes far beyond annotations by detailing fan-out behavior, response shapes (market, analysis, evidence), resolver contract, parent event extractor, news fields with fallback mechanisms, safety short-circuits, and resolution-rule risk. 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 detailed, which is necessary given the tool's complexity. It is structured with sections (RESPONSE SHAPES, RESOLVER CONTRACT, etc.) and front-loaded with the main purpose. However, it could be more concise; some details like fan-out examples could be summarized.

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 and no output schema, the description is extremely thorough, covering safety, error conditions (low-confidence, closed markets), response shapes, and resolution rules. It leaves minimal ambiguity for an AI 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 for all three parameters. The description adds extra context by providing examples for market input formats, explaining depth options, and detailing the include_raw behavior, thus adding significant value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: researching Polymarket bets by pulling Pipeworx data. It uses specific verbs like 'Research', 'pull', 'classifies', 'fans out', and 'returns', and distinguishes itself from sibling tools by focusing on general bet research with comprehensive fan-out.

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 ('should I bet on X', 'what does the data say about Y', 'is there edge in Z') and covers safety conditions like low-confidence resolutions and closed markets. However, it lacks an explicit 'when not to use this tool' statement, 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.

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

Beyond the annotations (readOnlyHint, openWorldHint, etc.), the description details the data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), specific metrics pulled, handling of off-calendar fiscal years, sorting by primary metric, and the inclusion of citation URIs. This far exceeds what annotations alone 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?

The description is a single dense paragraph that efficiently conveys all necessary information. While every sentence adds value, the structure could be improved with breaks or bullet points for faster scanning, but it remains clear and not overly 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 no output schema, the description adequately explains the return format (paired data + citation URIs, sorted by primary metric) and the data sources. For a tool with two parameters and two entity types, it covers all essential behavioral and output aspects without leaving major 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 description coverage, the description still adds significant value by clarifying the format of values for each entity type (e.g., tickers/CIKs for companies, drug names) and providing concrete examples. It also explains how the type parameter determines the data pulled, enriching the schema's minimal 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 starts with example queries like 'Compare X and Y' and explicitly states the tool performs side-by-side comparison of 2–5 companies or drugs in one parallel call. It clearly distinguishes from sequential single-pack lookups, making the purpose very specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes the directive 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing explicit guidance on when to use this tool over alternatives. It also gives example queries that signal the intended use cases.

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 safe, idempotent, read-only behavior. Description adds valuable context: returns verbatim evidence, confidence, source, citations, and explicit gaps for unanswerable facets. 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?

Every sentence serves a purpose, but slightly verbose. Front-loaded with main action and differentiator. Could trim some details without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, description thoroughly explains what the tool returns (structured findings, gaps, citations) and its behavior. Covers prerequisites, timing, and use cases, leaving little ambiguity for a complex 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 covers both parameters with descriptions. Description enhances by explaining depth mapping to facet counts (quick=3, standard=5, thorough=8) and plan restrictions, 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?

Clearly states it performs 'grounded multi-source research in one call' with decomposition and parallel routing. Distinguishes from sibling ask_pipeworx by specifying it's for broad/multi-part questions.

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 recommends for broad questions and contrasts with ask_pipeworx for single lookups. Mentions prerequisites (Pipeworx account, paid plan for thorough) and expected duration.

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 readOnlyHint, idempotentHint, and non-destructive. The description adds that results include full input schemas with curated examples and are ready to call, providing valuable 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 front-loaded with the core purpose and efficiently adds usage guidance and return value details. Every sentence is informative, though slightly lengthy, but justified given the tool's role.

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?

Although no output schema is present, the description explains what is returned (top-N tools with schemas). The input schema includes examples. The description is sufficient for an agent to understand how to use and what to expect from this discovery 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 the schema describes all parameters well. The description adds value by clarifying that query, q, task, search, and description are aliases, which helps agents understand flexibility.

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 'Find tools by describing the data or task' and lists numerous data domains, making the purpose specific and distinct from sibling tools which are about specific actions like querying data or managing 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?

The description explicitly advises using this tool when needing to browse or discover tools, and to call it FIRST when many tools are available. While it doesn't name alternatives, the guidance is clear and contextual.

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?

Discloses internal behavior including data sources (SEC, XBRL, USPTO, news, GLEIF), limitations (USPTO soft-fail), fallback (GDELT→GNews), and planned expansions. Annotations already declare readOnly and idempotent; description adds rich behavioral detail.

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 example queries and primary purpose. While moderately long, each sentence contributes essential information. Could be slightly tighter but no extraneous content.

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 details return fields and structure. Covers inputs, process, sources, limitations, and fallbacks. Adequate for agent to invoke 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 covers both parameters with descriptions. Description adds value by clarifying usage nuances (names not supported, use resolve_entity first) and providing examples. Some redundancy, but mostly additive.

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 defines the tool as providing a full cross-source profile of a US public company, supported by example queries. It distinguishes from sibling tools like resolve_entity and compare_entities by specifying when to prefer this tool over chaining 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?

Explicitly states when to use (holistic view request) and when to use alternatives (e.g., resolve_entity for name resolution). Provides clear context on preference over single-pack lookups.

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 aligns with annotations (destructiveHint: true, idempotentHint: true) but adds no new behavioral details beyond the obvious deletion. Annotations already cover safety profile, so this is adequate but not insightful.

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 declares purpose, second provides usage guidelines. No wasted words, perfectly 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?

For a simple single-parameter tool with complete schema and clear annotations, the description covers all needed context: what it does, when to use it, and how it relates to siblings.

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 a clear description for 'key'. Description adds no additional meaning beyond the schema. 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?

Description clearly states 'Delete a previously stored memory by key' with a specific verb and resource. It mentions pairing with remember and recall but does not explicitly differentiate when to use forget vs those tools beyond the usage guidelines.

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 three when-to-use scenarios: stale context, task completion, clearing sensitive data. Also mentions pairing with remember and recall, providing context for 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 confirm read-only, idempotent, non-destructive behavior. Description adds detail about fetching, extracting, and emitting markdown, aligning with annotations and giving additional process 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?

Two efficient sentences: first defines action and output, second lists use cases. No fluff, front-loaded with core 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?

Covers purpose, output format, and use cases adequately. With no output schema, description explains the result is a text blob. Lacks error details but acceptable given simplicity.

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%, so baseline is 3. Description repeats schema info for url and max_links without adding new semantic details beyond defaults and output note.

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 the tool generates an llms.txt file for any URL, using specific verbs and resources. It distinguishes itself from sibling tools like scan_competitor_ai_presence by focusing on llms.txt generation.

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 (client indexing, own project, competitor audit), providing clear context for when to use. Does not explicitly state when not to use, but no direct alternative exists among siblings.

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, indicating non-destructive read operations. The description adds that data comes from pypistats.org, but does not disclose potential rate limits or response structure. While not contradictory, it provides only minor additional behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, immediately stating the action and outcome. It is concise, front-loaded, and contains no superfluous 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?

Given one required parameter, no output schema, and clear annotations, the description adequately covers the tool's purpose. It lacks details on the return format (e.g., structure of download counts), but for a simple read-only tool this is not critical.

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 contains one parameter 'name' with a clear description. Since schema coverage is 100%, the description does not add new meaning beyond reaffirming the parameter's purpose. Baseline score 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 it retrieves download counts for a Python package with specific timeframes (last day, week, month) from a known source (pypistats.org). It distinguishes itself from sibling tools like 'get_package' or 'get_package_version' by focusing solely on download statistics.

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 use for gauging package popularity, which provides clear context. However, it does not explicitly mention when not to use it or suggest alternatives among the listed siblings, such as 'get_package' for more general package 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?

Annotations already declare idempotent, readOnly, and openWorld hints. The description adds context about the return payload (e.g., latest version, author, license), but does not disclose additional behavioral traits such as network dependency, rate limits, or caching beyond what annotations imply.

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 with three sentences: first sentence states purpose, second details return fields, third gives usage instruction. Front-loaded and efficient, with no unnecessary 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?

Given the simple tool with one parameter, full schema coverage, and output schema existence, the description covers the essential aspects. However, it lacks differentiation from similar siblings and does not mention potential network latency or external dependencies, which are partially addressed by 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?

The schema already describes the 'name' parameter with 100% coverage. The description reinforces the requirement for the exact pip package name and provides concrete examples ('requests', 'numpy'), adding clarity beyond the schema definition.

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 retrieves metadata for a Python package from PyPI, listing the specific fields returned. However, it does not explicitly differentiate from sibling tools like get_package_version, which might be used for a subset of this 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?

The description instructs to pass the exact PyPI package name with examples, but does not provide guidance on when to use this tool versus alternatives like get_package_version or scan_dependency. No exclusions or when-not-to-use advice is given.

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 safe read operation. Description adds specifics on returned fields (summary, Python version, dependency list, files) and mentions 'requires_dist', exceeding annotation info.

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 then returns. No wasted words, efficient and clear.

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 covers main return content. Includes example and dependency list explanation. Lacks error handling details, but overall 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 100%, parameters well-described in schema. Description reinforces with example but adds no new semantic details beyond 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?

Clear verb 'Get' and resource 'metadata for a specific version of a Python package on PyPI'. Distinct from siblings like get_package and list_releases, with explicit example.

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 'Use to inspect a pinned release like requests 2.31.0', implying context for when to use. No explicit exclusions, but sufficient for a targeted inspection tool.

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, and destructiveHint. Description adds that output is sorted and includes latest version, which is modest additional behavioral 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 with no extraneous words. Every sentence adds value: first defines the action and output, second provides typical usage scenarios.

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 adequately explains output format (list of version strings, sorted, plus latest). Could potentially clarify if versions are semver or just strings, but overall sufficient for a simple listing 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 covers 100% of parameters with description for 'name' (exact PyPI package name). Description reinforces this but doesn't add new parameter-level semantics beyond the schema, so baseline score 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?

Description explicitly states verb 'list' and resource 'published version strings for a Python package on PyPI'. It specifies sorted order and inclusion of latest version, clearly distinguishing from sibling tools like get_package or get_package_version which target different granularities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description provides explicit use cases: 'see a package's release history or check which versions are available to pip install'. It lacks explicit when-not-to-use guidance but the context clues from sibling tools imply 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 declare readOnly, idempotent, non-destructive. The description adds context that it returns specific fields and that it lists active subscriptions by default (with an option to include inactive). No contradictions. Additional details about auth (caller-specific) are implied but not explicitly stated.

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 cover purpose, return fields, and usage guidance. No extraneous words, well-structured.

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 explicitly lists return fields. It addresses the parameter implicitly and provides clear usage context. Sibling tools are covered by differentiation. Complete for understanding and using 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 a clear param description. The tool description reinforces that the default is active subscriptions but doesn't add new semantic meaning beyond the schema. 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 specifies the verb 'list', the resource 'subscriptions', and the scope 'caller's active subscriptions'. It lists the returned fields (id, type, params, etc.), and the use cases differentiate it from sibling tools like subscribe/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?

Explicit guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells when to use it and mentions related actions (adding more, cancelling), implicitly advising against using it for subscription or unsubscription.

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 rate limits (5 per identifier per day), cost (free), quota usage (doesn't count against tool-call quota), and processing cadence (digests read daily, affects roadmap). Annotations provide baseline readOnlyHint=false, etc., and description adds significant 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 a single paragraph of five sentences, front-loaded with the primary action. It logically flows from purpose to usage to behavioral details. No redundant phrases; 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 feedback submission tool with no output schema, the description fully covers what the agent needs: purpose, when to use each type, behavioral constraints, and parameter guidance. It is complete given the tool's simplicity and available schema 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 description coverage is 100% with detailed enum values and parameter descriptions. The description adds contextual guidance on how to formulate the 'message' (describe in terms of tools/packs, avoid pasting prompts), which enhances parameter semantics. Minor deduction because the description does not elaborate further on the 'context' object beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: sending feedback to the Pipeworx team. It specifies categories (bug, feature, data_gap, praise) and distinguishes from sibling tools by focusing on user feedback rather than functional queries or actions.

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 lists when to use each feedback type and provides important constraints: don't paste end-user prompts, describe in terms of Pipeworx tools/packs. It also mentions rate limits and quota impact, offering 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?

Annotations already declare the tool read-only, idempotent, and non-destructive. The description adds valuable behavioral context: data source ('CF analytics-engine'), no PII, and caching behavior ('5min-1h depending on window'). This goes beyond annotations and provides full 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 about 100 words across three sentences, each carrying essential information (purpose, use cases, data provenance). Front-loaded with the core function. No unnecessary words or repetition.

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 simple input schema (one optional parameter), full schema coverage, no output schema required (return type described in text), and comprehensive annotations, the description provides complete context for an agent to select and invoke the tool correctly, including data freshness and privacy.

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 a description for the only parameter. The tool description adds incremental value by linking window choice to use cases and caching duration. Baseline is 3 due to full schema coverage, but the extra context justifies a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns trending data (top tools, top packs, call volume) over a recent window, with a specific verb ('returns') and resource ('what other AI agents are calling on Pipeworx'). It distinguishes from siblings like 'discover_tools' or 'get_package' by focusing on aggregate trends rather than individual items.

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 lists three use cases: discovering hot data sources, confirming a canonical tool, and aligning use cases. It also provides guidance on window parameter choice ('shorter windows surface what's hot right now; longer windows show steady-state demand'). However, it does not explicitly mention alternatives among the large sibling set, though the use cases imply clear when-to-use scenarios.

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 read-only, idempotent, and non-destructive. The description adds significant behavioral context: acceptance of full URLs, Jaccard similarity filtering, placeholder checks, and fill-check mechanics. 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 dense but well-structured, front-loading the requirement and then explaining modes in logical order. While long, every sentence adds value for a complex tool. Some repetition (e.g., partition_check explanation appears twice) could be trimmed, 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?

Despite no output schema, the description fully documents the return format (opportunities[], partition_check, fill_check) and references sibling tool polymarket_fill_risk. Covers edge cases (empty args, placeholder slugs, low similarity) and provides actionable guidance for interpreting results.

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%, but the description substantially enriches both parameters with examples, mode-specific behavior, and detailed guidance (e.g., 'event slug like fed-decision-may-2026', 'topic: seed question'). This significantly aids correct 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 tool finds arbitrage opportunities on Polymarket using monotonicity violations and partition-sum checks. It distinguishes between single-event mode (event) and cross-event mode (topic), each with specific explanations, making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly states that exactly one of event or topic is required, recommends event for specific markets and topic for cross-event scanning, and provides usage examples. It does not explicitly contrast with sibling tools like polymarket_edges, but it does mention polymarket_fill_risk for custom sizing, which aids contextual decision-making.

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, openWorld, idempotent, non-destructive. The description adds extensive behavioral details: three model families with specific calculations (e.g., lognormal barrier, GDELT ratios, partition_overround corrections), caching (1h at KV level), exclusion of Fed bets, and diagnostics structure. 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 many details that could be condensed. While the structure with labeled sections helps, there is redundant or overly technical information that affects readability. It could be more concise while retaining essential points.

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 (9 parameters, no output schema), the description covers everything: response structure with segments, edge fields, diagnostics, caching, Fed note, and filter behavior. It is complete enough for an agent to understand what the tool returns and how to use it effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for all 9 parameters. The description adds extra context, such as explaining why min_kelly does not filter partition arbs and the purpose of min_partition_leg_kelly. This goes beyond the schema but is not fully comprehensive 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 it scans top Polymarket markets and returns opportunities where Pipeworx data disagrees with market price. It explicitly targets 'what should I bet on today' and describes three distinct segments with detailed logic, distinguishing it from siblings like polymarket_arbitrage (which likely focuses on cross-platform arbitrage).

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 the tool is for discovering betting opportunities without browsing hundreds of markets and mentions tradeable-edge knobs for filtering. However, it does not explicitly contrast with alternatives like polymarket_arbitrage or provide when-not-to-use scenarios, leaving some ambiguity.

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, destructiveHint. The description adds significant detail: returning tracked[], expired[], snapshot_dates[], explaining how decay is computed (from daily closes of edge_pp_net, not intraday), and noting history depth limits (60-day TTL) and snapshot gaps. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is detailed but front-loaded with the core purpose. Every sentence adds value, explaining response structure and limits. Slightly long but efficient for the information conveyed.

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 only 2 parameters, no output schema, and no nested objects, the description is very complete. It explains the response format thoroughly, including fields like first_seen, trend, decay_pp_per_day, expired opportunities with lifespan, and snapshot dates. All relevant details are covered.

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 both parameters described. The description adds default values, max bounds, and the meaning of 'window' (snapshot family). While helpful, it does not add substantial new meaning beyond the schema, 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 provides edge persistence and decay telemetry, answering whether an edge is shrinking or persistent. It differentiates from raw edge data (polymarket_edges) by focusing on time-series behavior across snapshots, distinguishing it 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 explicitly answers the question 'how long has this edge existed and is it shrinking?' and provides an example distinguishing fresh vs old edges. It implies usage for comparing edge persistence but does not explicitly list alternatives 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?

Despite annotations already indicating readOnlyHint=true, the description adds rich behavioral context: it is a check (not trade execution), walks the ladder, returns verdicts (clean|degraded|cannot_fill), and discloses risks like forced directional risk. 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 detailed and well-structured (bold headings, separate mode sections), but it is lengthy. Every sentence adds value, but slightly less concise than ideal. Still, the structure compensates.

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 the description enumerates all return fields (top_of_book, vwap_fill_price, etc.) for both modes. It covers prerequisites, parameter relationships, and risk warnings comprehensively, making it self-sufficient for agent understanding.

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 already covers all parameters with descriptions (100% coverage). The description adds further meaning: defaults (size_usd=1000, side auto for basket), acceptable ranges (clamp 10–1,000,000), and context for each mode, going well 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 defines the tool as a realizable-vs-theoretical edge check against live CLOB order-book depth. It distinguishes two distinct modes (single-market and basket/event), replicates return fields, and contrasts with sibling tools like polymarket_arbitrage and polymarket_edges by specifying when to use this tool before those.

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: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It also warns against partial basket fills and provides mode-specific parameter requirements, offering clear decision support.

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 the annotations (readOnly, idempotent, non-destructive), the description details safety fields: compatibility_warning, temporal_alignment, and skipped_cross_type/subtype counters. It explains when spreads are mathematically meaningful (aligned months) and when bet shapes are non-equivalent. 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 somewhat lengthy but well-structured, with the purpose front-loaded and key sections delineated. While verbose, the detail is necessary for a complex tool with two modes and multiple safety checks. Minor trimming could improve conciseness, but it 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's complexity and lack of output schema, the description comprehensively covers: two usage modes, response structure (leg-by-leg prices, top_spreads_pp), safety fields (compatibility_warning, temporal_alignment, skipped counters), and caveats about prevalence of warnings. It leaves little ambiguity about the tool's behavior and output.

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 already provides full descriptions for all parameters (100% coverage). The tool description adds rich context: it explains the two modes, lists all topic shortcut values, and clarifies that explicit parameters override topic mapping. This adds significant meaning beyond 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 clearly states the tool's purpose: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It distinguishes itself from sibling tools like polymarket_arbitrage and polymarket_edges by focusing on cross-venue comparisons, and it specifies two modes (topic shortcuts and explicit pairing).

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 'topic' mode versus explicit event tickers, and warns that pre-mapped topics often return compatibility warnings. It provides clear guidance on mode selection but does not explicitly contrast with alternative tools (e.g., for single-venue analysis).

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 and idempotent behavior. The description adds transparency by explaining the listing behavior when omitting the key and the scoping by identifier, complementing the 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?

Three concise sentences, front-loaded with the core functionality, no fluff. 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 simple retrieval tool, the description covers retrieval, listing, scoping, and relationships with remember and forget. No output schema is needed given the simplicity.

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 'key' is fully described in both the schema and the description, including the nuance that omitting it lists all keys. Schema coverage is 100%, and the description adds no redundancy.

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 retrieves a previously saved value or lists all saved keys. It distinguishes itself from sibling tools like 'remember' and 'forget' by specifying the retrieval nature.

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 advises using this tool to look up stored context without re-deriving it, providing clear use cases. However, it does not explicitly state when not to use it or mention alternative tools.

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 the return structure (source, citation_uri, raw event payload) and explains the mark_read side effect, which is beyond what annotations provide. It is transparent about filter behavior and polling suitability. Note: readOnlyHint annotation is contradicted by the mark_read parameter causing state changes, but the description accurately describes this.

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 composed of 5 sentences, each adding value. It is front-loaded with the main purpose and provides necessary details without redundancy. 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?

Despite no output schema, the description explains return values (source, citation_uri, payload), covers all parameters, and provides additional context (REST alternative, polling suitability). This is comprehensive 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 schema already documents all 5 parameters with descriptions (100% coverage). The description adds value by explaining the purpose of 'type' and 'since' filters, specifying the default and max for 'limit', and clarifying the 'mark_read' effect. This surpasses the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it pulls fired events from the subscription feed and returns recent alerts. It uses specific verbs like 'pull' and 'returns', and identifies the resource as alerts from a feed. The tool is distinct from siblings like 'recent_changes' or '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?

The description provides clear context for use: it is for reading alerts from the feed, with filtering options. It mentions that the same feed is accessible via REST for scripts and dashboards, implying an alternative. However, it does not explicitly state when not to use this tool or contrast it with sibling tools.

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?

Describes fallback behavior (GDELT to GNews), status of USPTO (soft-fails until reactivated), date format flexibility, and return structure. Adds significant context beyond read-only, open-world, idempotent 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 dense but well-structured, starting with natural language examples, then fan-out details, then parameter specifics, and ending with sibling differentiation. Every sentence adds value, though it could be slightly more organized.

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 (multi-source, fallback, soft-fail), the description covers return format (grouped changes, total count, citation URIs), parameter usage, and known limitations. No output schema, but description sufficiently describes output.

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 extra clarity: explains 'since' can be ISO or relative shorthand, recommends typical values, and clarifies 'value' accepts ticker or CIK.

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 tool's purpose is clearly stated with multiple example queries and explicit mention of combining SEC EDGAR, GDELT/GNews, and USPTO sources. It directly distinguishes itself from sibling tool 'entity_profile'.

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 guidance on when to use this tool versus 'entity_profile' for static profiles. Also suggests typical monitoring windows like '30d' or '1m'.

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 idempotentHint=true and destructiveHint=false. Description adds meaningful behavioral context: key-value scoping by identifier, persistence differences for authenticated vs anonymous users (24-hour retention). Does not contradict 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?

Four sentences, each earning its place: purpose, usage trigger, technical details, pairing. Front-loaded with action verb and purpose, minimal waste.

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 two-parameter key-value store with no output schema, description covers purpose, usage, scoping, persistence, and lifecycle (retrieve/delete). Complete and adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear descriptions for both key and value. Description adds no additional parameter details beyond schema, 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?

Description starts with specific verb 'save data' and resource 'memory', explicitly states purpose for reuse across sessions. Distinguishes from sibling tools (recall, forget) by naming them and contrasting behavior.

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 for retrieval/deletion, giving clear guidance on 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 (readOnlyHint, idempotentHint, destructiveHint) already indicate safe, read-only behavior. The description adds value by revealing internal cascading lookups and stating that it replaces 2-3 manual calls, providing transparency about what happens during execution without contradicting 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: starts with conversational examples, then a concise purpose statement, followed by detailed breakdown per type. Every sentence adds value without redundancy, earning its place despite moderate 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 tool's simplicity (2 parameters, no output schema) and comprehensive description covering input formats, return values, and internal behavior, the description is complete. There are no gaps that would hinder an agent's understanding or correct invocation.

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 covers both parameters with descriptions (100% coverage), so baseline is 3. The description adds examples (e.g., 'AAPL', '0000320193', 'ozempic') and mentions auto-disambiguation, which enriches the semantic understanding beyond 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 clearly states the tool's purpose: resolve a user-spoken name to a canonical/official identifier. It provides specific examples of queries and explicitly lists supported entity types (company, drug) with their respective return fields, distinguishing it from sibling tools like compare_entities or entity_profile.

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 gives explicit guidance: 'Use FIRST whenever you have a name but need an ID.' It also details acceptable input formats for each type (ticker, CIK, name for company; brand/generic name for drug) and notes that it replaces multiple manual lookups, helping the agent decide when to invoke it.

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, destructiveHint=false. Description adds that each entity is probed with ai_visibility_check and results are ranked, complementing annotations 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?

Two front-loaded sentences: first states purpose, second gives use case and output. No extraneous 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?

With 4 parameters, no output schema, and sibling tools, description explains the internal use of another tool and output format. Could mention entity count range (2-8) but is otherwise 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. Description adds context about entities being brands/competitors and output fields, but does not elaborate on individual parameter formatting 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 states a specific verb ('Compare') and resource ('AI visibility across multiple entities'), and distinguishes from sibling tool 'ai_visibility_check' which is used per-entity. It clearly explains the ranking and output.

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 use case ('competitive AI-marketing audits') and implies when to use this tool vs a single-entity probe. Could add 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?

Discloses key behavioral traits beyond annotations: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list timeout sources. Annotations already mark it as read-only, idempotent, and non-destructive, so the description adds valuable runtime context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but well-structured: front-loaded with purpose, then usage, then behavior, then ecosystem scope. Every sentence adds value, though it could be slightly more scannable with bullet points. Still efficient for an AI agent.

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 composite tool with two data sources, partial failures, and no output schema, the description fully covers output fields (summary block, advisory details, links, alternatives), timing constraints, and ecosystem limitations. It leaves no major 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 the description adds meaning: explains that scoped packages (e.g., '@types/node') are accepted for the 'package' parameter, and that 'version' defaults to latest when omitted. It also provides a concrete example ('18.3.1'), 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 performs a composite safety/size/popularity check for npm packages, combining data from deps.dev and bundlephobia. It specifies the exact resource (npm packages) and verb (scan/check), and distinguishes itself from sibling tools like get_package or get_package_version by emphasizing it's a single aggregate call.

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 (e.g., 'is X safe / popular / small') and provides an alternative for non-NPM ecosystems: 'PyPI / Maven / Cargo / Go fall under deps.dev:version directly.' This gives clear decision context and exclusions.

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 safety hints (readOnlyHint, etc.). The description goes beyond by detailing truncation at 200K chars with a flag, embedding model (BGE-base-en), window size (500-char overlapping), and cosine similarity. 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 five sentences, highly informative, and front-loaded with purpose. Every sentence adds necessary detail 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 the complexity of semantic search and the lack of an output schema, the description covers the tool's operation, behavioral details, usage scenario, and pairing with a sibling. It sufficiently informs the agent about return values (passages with offsets and scores).

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 the description adds value by providing examples of text types (SEC filing, article) and query examples, and clarifies the truncation cap. This builds on the schema's 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 semantic search inside a fetched record, using specific verbs like 'search inside' and 'return top-N passages'. It distinguishes itself from siblings by mentioning its pairing with ask_pipeworx_grounded.

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 to use when a record is too large for the prompt and provides context for pairing with ask_pipeworx_grounded. However, it does not explicitly state when not to use this tool, leaving some room for ambiguity.

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?

Discloses side effects (persistent subscription, SMS cap, webhook auto-disable) and authentication needs, adding context beyond annotations. Does not cover deletion or cost, but good overall.

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?

Well-structured with purpose, requirements, types, and delivery details front-loaded. Efficient but slightly lengthy; every sentence is justified.

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 subscription types and delivery channels comprehensively. No output schema, but returns subscription id is mentioned. Could detail response format, but generally complete for complexity level.

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?

Adds concrete examples and use cases for each type (e.g., 'items:["5.02"] = officer change') beyond schema descriptions, which only provide minimal detail. Schema coverage is 100%, so additional context elevates the score.

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 'Create a proactive monitoring subscription to a live-data event stream' with specific verb and resource. Distinguishes from sibling tools like 'list_subscriptions' and 'unsubscribe' by detailing subscription creation and types.

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 requirement of Pipeworx OAuth account and lists supported types with parameters, helping the agent decide when to use. However, lacks explicit when-not-to-use guidance relative to 'recent_alerts' or 'list_subscriptions'.

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, and destructiveHint as false, so the tool's safety profile is clear. The description adds that it returns example questions with tool shapes, but does not disclose additional behavioral traits like rate limits or side effects. The description does not contradict 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?

The description is a single paragraph but somewhat verbose, starting with a list of example queries before stating the purpose. It is front-loaded with the core purpose, but the example list could be trimmed to improve conciseness. Every sentence is relevant, but it could be tighter.

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 simplicity (one optional parameter, no output schema) and rich annotations, the description provides adequate context: it explains the return format, usage scenario, and connection to a live catalog. It is complete enough for an agent to decide when to call this 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% for the single optional parameter 'topic'. The description adds usage context (e.g., 'Call with no arguments for the full spread, or pass topic to focus') and gives example values similar to the schema. This adds marginal value but does not provide new semantic information beyond what the schema already offers.

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 it is the onboarding entry point, returns category-bucketed example questions with exact tool+argument shapes, and distinguishes itself from sibling meta-tools like ask_pipeworx, entity_profile, and compare_entities. This makes its purpose very specific and clear.

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 says 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' This provides clear context on when to use it. It does not explicitly state when not to use it, but the guidance is strong and implies it is for initial discovery.

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?

Discloses deactivation over deletion and links to recent_alerts for history. Annotations confirm non-destructive and idempotent behavior; description adds meaningful behavioral details 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, each serving a purpose: action, ownership constraint, and data lifecycle. No redundant words. Front-loaded with main action.

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 required param and no output schema, the description covers all key aspects: purpose, ownership, data impact, and historical availability. 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?

Schema description covers the id parameter fully (100% coverage). Description adds no additional parameter information, so baseline score of 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?

Clearly states verb 'cancel' and resource 'subscription by id'. Ownership enforcement distinguishes from siblings like subscribe and list_subscriptions. No ambiguity.

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 context on when to use (cancelling own subscriptions) and implies alternatives via sibling tools. Does not explicitly list when not to use but sufficient given 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?

Annotations already declare readOnly, idempotent, non-destructive. Description adds substantive context: data sources (SEC EDGAR + XBRL), return fields (verdict types, citation, delta), and that it replaces 4-6 calls. 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?

Description is moderately long but front-loaded with examples. Every sentence adds value: use cases, supported domains, return values. Could be slightly more concise but well-structured for a complex 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?

Despite no output schema, the description details return fields (verdict, structured form, actual value with citation, delta). Also notes limitations and scope. Complete for a single-parameter verification 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 a concise description of the 'claim' parameter. The tool description enriches it with examples and format guidance, 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 states the tool does natural-language claim verification with specific examples and domain (company-financial via SEC EDGAR). It clearly distinguishes from siblings by being a dedicated verification tool that replaces multiple sequential calls.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' Provides examples of queries. Notes v1 limitation to company-financial claims, implying when not to use for other domains.

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