Meteors

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description adds valuable behavioral context: default model is free, passing _apiKey incurs user costs, and returns per-model object and combined view. 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?

Three sentences: first defines main action, second adds default and API key nuance, third lists returns and use cases. Front-loaded, no wasted words. Perfectly concise and 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?

For a 4-param tool with no output schema, the description covers purpose, parameters, return structure (per-model fields + combined view), and use cases. Could add examples or signal details, but overall complete for informed usage.

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

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

Schema coverage is 100% with descriptions for all 4 parameters. The description enriches parameter meaning by explaining default model behavior for 'models', BYO key for '_apiKey', and disambiguation use for 'context'. Adds value beyond schema.

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

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

Description clearly states the tool probes LLMs for knowledge about an entity and scores visibility (0-100). It specifies the resource (LLMs), action (probe and score), and distinguishes from siblings like scan_competitor_ai_presence by focusing on visibility scoring across models.

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

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

The description provides explicit use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It mentions default model and optional Anthropic usage with BYO key, but does not explicitly state when not to use or compare to alternative tools, though the context is clear.

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

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

The description discloses that the tool selects the best data source and fills arguments automatically, which is important behavioral context. It implies a question-answering behavior but does not detail limitations, error handling, or whether it can handle complex multi-step requests. Since no annotations are provided, the description carries the full burden, and it provides moderate 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 very concise at two sentences plus examples. It is front-loaded with the core purpose, immediately followed by how it works and examples. Every sentence adds value, and there is no redundant 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 simplicity of the tool (single parameter, no output schema), the description is fairly complete. It explains what the tool does, how it works, and provides examples. It could be improved by mentioning what types of questions are out of scope or how it handles ambiguous queries, but for the complexity level, it is 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?

The description adds significant meaning beyond the input schema: it explains that the 'question' parameter should be a natural language request, and provides examples. The schema already has 100% coverage with a description for the parameter, but the description enriches it with usage context and examples, earning a score above baseline.

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

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

The description clearly states the tool's purpose: it takes a plain English question and returns an answer from the best available data source. It distinguishes itself from siblings by emphasizing natural language queries, which is unique among the listed sibling tools that are more structured (e.g., get_fireballs, get_neo_feed).

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 guidance on when to use this tool: when you want to ask a question in plain English without needing to browse tools or learn schemas. It implicitly excludes structured data queries (which should use sibling tools) and gives examples of appropriate usage. 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?

Annotations already indicate read-only, open-world, and non-destructive behavior. The description adds valuable process context (resolve, classify, fan out, return evidence) and frames it as the 'core demo product.' It 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?

The description is concise (4 sentences) and front-loaded with the purpose. Every sentence adds value, from input options to output and use cases. 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 the return value ('evidence packet plus ... market-vs-model comparison'). It covers the workflow and use cases. However, it could detail the evidence packet structure for completeness.

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

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

Schema description coverage is 100%, so the baseline is 3. The description reiterates the 'market' parameter's flexibility (slug, URL, text) but does not add meaning beyond the schema's description. The 'depth' parameter is explained only in 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: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies input types (slug, URL, question text) and the output (evidence packet with comparison). This distinguishes it from sibling tools like ask_pipeworx or compare_entities by its focus on betting research.

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: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?"' It implicitly differentiates from siblings, but does not explicitly state when not to use it or list 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?

With no annotations, the description must carry full weight. It mentions data sources (SEC EDGAR, FDA) and return format (paired data, URIs), but lacks details on side effects, auth requirements, or rate limits.

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 clear, front-loaded sentences with no redundancy. Every sentence provides essential information efficiently.

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

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

Given no output schema, the description explains return data sufficiently for a comparison tool. However, it could benefit from an example return or more detailed format description.

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 all parameters well (100% coverage). The description adds meaning by specifying what data fields are returned for each entity type, complementing the schema.

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

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

The description clearly states it compares 2-5 entities side by side, specifies two entity types with distinct data fields, and highlights efficiency gains. This distinguishes it from siblings like get_fireballs or resolve_entity.

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 explains the tool replaces multiple sequential calls and hints at when to use (comparison tasks). However, it doesn't explicitly state when not to use or list alternative tools for similar purposes.

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?

No annotations provided, so description carries full burden. It states the tool returns 'the most relevant tools with names and descriptions,' which is transparent. However, it doesn't disclose any limitations like whether the search is semantic or keyword-based, or if it handles all 500+ tools equally. Minor gap but still good for a search tool.

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

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

Description is two sentences: first states purpose, second gives usage guidance. Efficient and front-loaded. However, could be slightly more concise by removing 'Call this FIRST' redundancy with 'FIRST' capitalization, but still 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?

Given no output schema, description explains return value: 'Returns the most relevant tools with names and descriptions.' It also provides context for when to use (500+ tools). For a simple search tool, this is mostly complete. Could mention if results are ranked, but 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?

Schema coverage is 100%, with descriptions for both parameters. The description does not add extra meaning beyond the schema. For 'query,' the schema gives examples, and the description adds context about 'natural language.' 'limit' is well-described. Baseline 3 is appropriate since schema does the heavy lifting.

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: 'Search the Pipeworx tool catalog by describing what you need.' It specifies the action (search), the resource (tool catalog), and the input method (natural language description). It also distinguishes itself by instructing to call it 'FIRST' when 500+ tools are available, implying it's a discovery tool that complements the other specific tools like get_neo_feed.

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 this tool: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This gives clear usage context and implies alternatives (other tools) are for specific tasks after 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?

Despite no annotations, the description discloses that it bundles multiple data sources, returns citation URIs, and mentions performance implications (too slow to include federal contracts). It implies a read-only operation but could explicitly state non-destructiveness.

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 efficiently convey purpose, contents, and usage guidance. No redundant information; every sentence adds value. Front-loaded with the main 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?

Reasonably complete given complexity and lack of output schema. Describes the major data categories returned and offers comparative context. Could mention potential response size or rate limits, but 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?

Adds valuable context beyond the schema: explains that only 'company' is supported (with future plans), specifies that value accepts ticker or CIK (not names), and directs users to resolve_entity for name resolution. The schema already has good coverage, but the description enhances understanding.

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

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

Description explicitly states the tool returns a full profile of an entity across Pipeworx packs in one call, listing specific data sources (SEC filings, XBRL, patents, news, LEI) and noting it replaces 10-15 sequential calls. It clearly distinguishes from siblings by mentioning when to use usa_recipient_profile instead.

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 context on when to use (for company profile), what identifiers are valid (ticker or CIK, not names), and a specific alternative (usa_recipient_profile for federal contracts). It also suggests using resolve_entity if only a name is available.

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

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

With no annotations, the description correctly identifies this as a delete operation. However, it lacks details such as whether the deletion is permanent, if confirmation is required, or any side effects.

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, clear sentence that directly conveys the purpose without any unnecessary words.

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

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

Given the tool's simplicity (1 required parameter, no output schema), the description is adequately complete. It could optionally note that the operation is irreversible, but the current level suffices.

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

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

The description mentions 'by key', which adds context to the 'key' parameter beyond the schema's 'Memory key to delete'. Since schema coverage is 100%, this is a helpful addition.

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

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

The description clearly states the verb 'Delete' and the resource 'stored memory', and the qualifier 'by key' distinguishes it from sibling tools like 'remember' and 'recall'.

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

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

The description implies usage for deleting a specific memory by key, but does not explicitly contrast with alternatives like 'remember' or 'recall', nor does it mention when to use this tool over others.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, so description does not contradict. The description adds behavioral detail: it 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' This discloses the non-destructive fetch-and-extract behavior 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 two sentences: the first clearly states the tool's action and output format, the second lists concrete use cases. It is front-loaded with the essential purpose, no wasted words, and every sentence adds value.

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

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

Despite the absence of an output schema, the description fully explains the output format ('standard llms.txt markdown format' and 'single text blob ready to drop at site-root/llms.txt'). It covers input parameters implicitly (URL and max_links), process, output, and use cases. For a tool with 2 simple parameters, this is 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?

Input schema has 100% coverage; both parameters (url, max_links) already have clear descriptions in the schema. The description does not add new parameter-specific information beyond what is in the schema. Baseline score of 3 applies as schema does the heavy lifting.

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: generating a production-ready llms.txt file for any URL. It specifies the verb 'Generate', the resource 'llms.txt file', and explains what it does (fetches page, extracts metadata, emits standard format). It distinguishes itself from siblings (unrelated tools like bet_research) by its unique function.

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

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

The description provides explicit use cases: 'getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.' This gives clear context for when to use the tool. It does not explicitly state when not to use or mention alternatives, but the sibling list lacks similar tools, so guidance is adequate.

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?

No annotations are provided, so the description carries the full burden. It discloses the return fields (name, date, miss distance, velocity, diameter), which informs the agent about output. However, it does not mention side effects, rate limits, or whether results are sorted or filtered beyond the distance criterion. The distance threshold is a key 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?

Two sentences with clear, front-loaded purpose. First sentence states the core functionality, second lists outputs. No filler, but could be slightly more efficient by combining them. Appropriate length for a simple tool with one optional parameter.

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 compensates by listing return fields. The tool has only one optional parameter with full schema coverage, so the description is largely sufficient. However, it lacks details on pagination, sorting, or the timeframe of events (past/future), which could be important for the agent to use correctly.

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

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

Schema description coverage is 100%, so baseline is 3. The description does not add meaning beyond the schema for the 'limit' parameter; it does not explain how the limit affects results (e.g., ordering) or mention any other implicit parameters. No additional param info in description, so score remains at baseline.

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

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

The description clearly states the verb 'Get', the resource 'near-Earth asteroid close approach events', and provides specific criteria (within 0.05 AU of Earth). It also lists the returned data fields, making the purpose unambiguous. Distinguishes from sibling tools like get_fireballs (different celestial phenomena) and get_neo_feed (likely a different NEO dataset).

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

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

The description implies usage for retrieving close approach data but does not explicitly state when to use this tool versus alternatives. No exclusion criteria or context about when not to use it are provided. The 'within 0.05 AU' constraint offers some guidance but is not compared with other 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?

Annotations are empty, so the description carries the burden. It correctly discloses that the tool returns data about fireball events with specific fields (energy, velocity, altitude, location). However, it does not mention any behavioral traits such as read-only nature, data freshness, or potential empty results.

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, clearly stating the purpose and the data fields. Every sentence is informative with no wasted words.

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

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

Given the tool's simplicity (one optional parameter, no output schema needed), the description is adequate. It explains what data is returned, which is sufficient for an agent to decide to invoke 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 description coverage is 100%, and the description adds value by mentioning the types of data returned, but it does not elaborate on the 'limit' parameter beyond what the schema already provides (default 10, max 100).

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 uses a specific verb ('Get') and resource ('recent bolide and fireball events'), and clearly distinguishes itself from sibling tools like 'get_close_approaches' and 'get_neo_feed' by focusing on fireballs recorded by US government sensors.

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

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

The description implies usage for retrieving fireball events but provides no explicit guidance on when to use this tool versus alternatives like 'get_close_approaches'. No exclusion criteria or prerequisites are mentioned.

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?

No annotations are provided, so the description carries full burden. It discloses the API source (NASA NeoWs) and what data is returned (names, sizes, velocities, etc.), but does not mention rate limits, authentication needs, or whether the operation is read-only. Without annotations, this is adequate but not comprehensive.

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 with no waste. The first sentence states purpose and API source; the second lists key return fields. 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?

The tool has 2 required parameters, no output schema, and no annotations. The description covers purpose and return fields, which is sufficient for a simple date-range query. However, without output schema, the agent lacks details on return structure (e.g., pagination, max results). Adequate but minimal.

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 the schema already documents both parameters with format and constraints. The description adds no extra meaning beyond what the schema provides, 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 uses specific verbs ('Get') and resources ('Near-Earth Objects') and clearly distinguishes this tool from siblings by mentioning NASA NeoWs API and listing return fields. It avoids tautology and clarifies scope (passing by Earth, date range).

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 implicitly states when to use (for NEO data in date range) but provides no explicit exclusions or alternatives. Sibling tools like get_close_approaches exist but are not mentioned as alternatives, leaving the agent to infer from names.

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?

No annotations are provided, so the description must disclose behaviors. It mentions rate limiting (5 per day) and that the tool is free, but it does not explain what happens after sending (e.g., confirmation, response format) or address data handling. This is adequate for a simple feedback tool but leaves gaps.

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: three sentences covering purpose, usage guidelines, and behavioral constraints. It front-loads the action and avoids 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 tool's simplicity (3 parameters, nested object, no output schema), the description covers purpose, usage, rate limits, and what to avoid. Minor omission: no mention of what the agent receives in response, but this is not critical for a feedback 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 description coverage is 100%, meaning the input schema thoroughly explains each parameter. The description adds no additional semantic value beyond reinforcing that context should relate to Pipeworx tools/data. 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 uses the verb 'Send feedback' and specifies the resource 'Pipeworx team'. It lists specific use cases: bug reports, feature requests, missing data, and praise, which clearly distinguishes this tool from siblings that deal with queries or data retrieval.

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 states when to use the tool (for feedback to the Pipeworx team) and provides a negative instruction (do not include end-user's prompt verbatim). While it does not explicitly mention alternatives, no sibling tool serves the same purpose, making the guidance clear.

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

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

Describes internal logic: walks child markets, extracts dates/thresholds, sorts, reports violations. Annotations already indicate readOnly and openWorld, but description adds valuable behavioral context without contradiction.

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

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

Single paragraph is efficient and informative. Could be structured into clearer sentences, but no wasted words. Front-loads purpose effectively.

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 states return format. Parameter explained sufficiently. No gaps remain for an agent to understand usage.

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

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

Only one parameter ('event') with 100% schema coverage. Description adds details beyond schema: acceptable formats (slug or URL), explains how tool processes input. Highly informative.

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

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

Clearly states the tool's purpose: finding arbitrage opportunities within a Polymarket event by detecting monotonicity violations. Distinguishes from siblings like 'polymarket_edges' which likely handles cross-event edges.

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

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

Explicitly describes when to use the tool (for same-event arbitrage due to price ordering constraints). Could improve by stating when not to use (e.g., cross-event comparisons), but clear context.

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

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

Annotations already indicate readOnlyHint and openWorldHint, but the description adds valuable behavioral context: it details the V1 model (lognormal from FRED + coinpaprika), the fetch strategy (once per asset), and the ranking by |edge|. Nothing contradicts 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 four sentences, front-loaded with purpose, and contains necessary details. It is slightly verbose but well-structured; could be trimmed slightly without losing essential info.

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 (model-based, multiple steps) and lack of output schema, the description covers the pipeline adequately (fetch, model, rank, output). It explains the output format (top N with trade direction). It does not mention performance or rate limits, but the core functionality is clear.

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

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

Schema coverage is 100% with clear descriptions for limit, window, and min_edge_pp. The description does not add additional meaning beyond the schema, 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?

The description clearly states the tool scans high-volume Polymarket markets, identifies those where Pipeworz data disagrees with market price, and returns top edges. The verb 'scan' and the resource 'Polymarket markets' are specific, and it distinguishes from siblings like polymarket_arbitrage which serves a different purpose.

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 specifies it's built for the 'what should I bet on today' question, helping agents discover opportunities without manual browsing. While it provides clear context for when to use, it does not explicitly mention when not to use or list alternatives, but the use case is well-defined.

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?

No annotations provided, so description must carry full burden. It states the tool can retrieve by key or list all, which is clear. However, it does not disclose whether retrieval is destructive or any side effects, nor any access restrictions. Since it's a read operation, the behavioral disclosure is adequate but not exceptional.

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 two sentences, concise and front-loaded. Every sentence adds value: first sentence defines action, second explains when to use. Could be slightly more concise by merging, but overall efficient.

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

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

Given no output schema and one optional parameter, the description covers the tool's functionality well. It explains the dual behavior (retrieve vs list) and provides usage context. Could mention return format, but not necessary for a simple retrieval 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%, and description adds meaningful context: explains that omitting key lists all memories. This goes beyond the schema's description of 'key' as a string.

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 verb 'Retrieve' and resource 'stored memory', and distinguishes between retrieving a specific key vs listing all memories. Sibling tools include 'remember' (store) and 'forget' (delete), so this tool's purpose is well differentiated.

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

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

Description says to use for retrieving context saved earlier, which gives context but no explicit when-not-to-use or alternatives. However, the sibling tools provide implicit differentiation.

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?

No annotations are provided, so the description carries full behavioral disclosure burden. It reveals fan-out to three data sources (SEC, GDELT, USPTO) executed in parallel, accepted date formats, and return structure (structured changes, count, URIs). It also notes the limitation that only 'company' type is supported. This provides substantial behavioral insight, but omits potential costs (rate limits, latency) or authentication requirements.

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 (four sentences) and well-structured. It front-loads the purpose, then details the fan-out behavior, date formats, and return value. Every sentence provides essential information with no redundancy or fluff.

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

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

Given the lack of output schema, the description adequately explains return values (structured changes, total_changes count, URIs). It covers the key context: parallel queries, date acceptance, and entity type constraint. All critical aspects for agent decision-making are addressed.

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

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

The input schema has 100% description coverage, providing baseline of 3. The description adds value by explaining the 'value' parameter accepts ticker or CIK, elaborating on the 'since' parameter with examples (ISO date, relative formats), and confirming the only allowed type. This enhances understanding beyond the schema itself.

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 function: to retrieve changes about an entity since a given time. It specifies verb ('what's new'), resource ('entity'), and scope ('since a given point in time'). It implicitly distinguishes from siblings like entity_profile or compare_entities by focusing on temporal changes.

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 suggests use cases: 'brief me on what happened with X' or change-monitoring workflows. However, it does not mention when not to use this tool or provide alternatives among sibling tools, which would strengthen 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?

With no annotations provided, the description carries full burden for behavioral transparency. It discloses persistence behavior (authenticated vs. anonymous) and the purpose of the memory, which is sufficient for a simple key-value store.

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 that are front-loaded with the core action and include essential usage context. 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?

For a simple key-value store with no output schema and fully described parameters, the description is complete. It covers purpose, usage, and persistence behavior.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds value by explaining the purpose of storing values (intermediate findings, preferences) and the nature of the key (e.g., 'subject_property'), providing context beyond the schema's example values.

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

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

The description clearly states the verb ('store'), the resource ('key-value pair in your session memory'), and the scope ('session memory'), which is distinct from sibling tools like 'forget' and 'recall'.

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

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

The description provides explicit guidance on when to use this tool ('save intermediate findings, user preferences, or context across tool calls') and distinguishes between authenticated and anonymous sessions, but does not mention when not to use it or 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?

With no annotations, the description carries full burden. It describes the resolution behavior, accepted inputs, and return fields including URIs. It does not mention side effects, auth, or errors, but for a read-like operation this is adequate. Score 4.

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 (three sentences) with no superfluous information. Each sentence adds purpose, usage, or benefit. Structure is front-loaded with the main action. Score 5.

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 resolution tool with no output schema, the description is complete: it explains inputs, outputs, and the benefit. It covers the necessary context for an agent to invoke correctly. Score 5.

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 descriptions cover 100% of parameters, so baseline is 3. The description adds value by including examples and explaining the return format (ticker, CIK, name, URIs), which goes beyond the schema. Score 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 resolves entities to canonical IDs, gives a specific example for 'company' type, lists accepted inputs, and mentions replacement of multiple calls. However, it does not explicitly differentiate from sibling tools, so score 4.

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 indicates this tool should be used for single-call resolution, replacing 2-3 lookup calls. It sets context for v1 only supporting 'company', but lacks explicit when-not or alternative tool names. Score 4 for clear context without 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 declare readOnly, openWorld, idempotent. Description adds that it calls ai_visibility_check per entity, ranks results, and returns score/confidence/signal density. No contradiction.

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

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

Three sentences, front-loaded with main purpose, 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?

Covers inputs, behavior (per-probe mechanism), and output (ranked list with score, confidence, signal density). No output schema, but description adequately describes returns.

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

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

100% schema coverage. Description adds semantics: first entity treated as 'subject' for narrative, models default to 'workers-ai' if omitted, and _apiKey required only if 'anthropic' is included.

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 verb ('Compare'), resource ('AI visibility across multiple entities'), and distinguishes from sibling 'ai_visibility_check' by specifying it probes multiple entities side-by-side and ranks them.

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 use case: 'competitive AI-marketing audits' with example question. Does not explicitly mention when not to use or alternatives, but the context strongly implies it's for multi-entity comparisons.

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?

No annotations provided, so description takes full burden. It details the return types (verdict, structured form, actual value, citation, delta) and mentions it replaces multiple calls. Does not discuss limitations or authentication, but overall transparent.

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-loading purpose, then scope, then output. Every sentence adds value with no redundancy.

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

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

Given single parameter and no output schema, description adequately explains what the tool returns (verdict, structured form, value, citation, delta). Covers the essential aspects.

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

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

Single parameter 'claim' with 100% schema coverage. Description adds examples and clarifies it's natural-language, going beyond the schema description.

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

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

Description clearly states it fact-checks natural-language claims against authoritative sources, specifying scope (company-financial) and distinguishing from siblings by noting it replaces multiple agent 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 it supports company-financial claims for public US companies via SEC EDGAR + XBRL, providing clear context for when to use. Lacks explicit when-not-to-use or alternatives, but sufficient for the domain.

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