Mapbox

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

Adds value beyond annotations by explaining it routes to 2,520 tools across 575 sources, fills arguments, and returns structured answers with pipeworx:// citation URIs. Annotations already declare read-only and non-destructive, and the description aligns well.

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

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

The description is front-loaded with the key instruction and is well-organized by domains and examples. While slightly verbose, every sentence adds value; it could be tightened but remains clear and informative.

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 (one parameter, many sub-tools), the description covers purpose, usage, behavior, parameter meaning, and output format (structured answers with citations). No output schema exists, but the description sufficiently explains what the tool returns.

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

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

Schema coverage is 100% for the single 'question' parameter. Description enriches the parameter by listing many example question types (e.g., 'current US unemployment rate', 'Apple's latest 10-K'), adding practical context beyond the schema's generic 'natural language' description.

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

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

The description clearly states the tool routes factual questions to appropriate structured data sources, contrasting with web search. It specifies the verb 'ask' and the resource 'pipeworx' with detailed domain examples, distinguishing it from siblings implicitly via its meta-tool 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?

Explicitly says 'PREFER OVER WEB SEARCH' and lists when to use (factual questions about real-world entities/events/numbers) with concrete examples. It advises use even when web search could answer, covering both when and why this tool is preferred.

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 openWorldHint=true. The description adds behavioral details beyond annotations: it explains the internal steps (resolving market, classifying bet, fanning out to packs) and the output (evidence packet + comparison). It does not mention any destructive actions, consistent with destructiveHint=false.

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

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

The description is a single paragraph of five sentences, each adding value. It front-loads the purpose and input formats, then explains the process and output. Slightly wordy with a marketing-like final sentence, but overall efficient 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?

No output schema is provided, so the description must cover return values. It mentions 'evidence packet plus a simple market-vs-model comparison,' which is sufficient for understanding but lacking details on the structure of the packet or comparison. Given the tool's complexity, this is adequately complete for its purpose.

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 two parameters with complete descriptions. The description adds significant meaning: it clarifies that the 'market' parameter accepts three formats (slug, URL, question text) and that 'depth' defaults to 'thorough' and controls the number of evidence sources. This enriches the schema without repetition.

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 researches Polymarket bets by pulling Pipeworx data, specifies multiple input formats (slug, URL, question text), and describes the output: evidence packet plus market-vs-model comparison. It distinguishes from siblings like 'ask_pipeworx' and 'polymarket_edges' by positioning itself as a one-call solution for bet context.

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

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

The description gives explicit use cases ('should I bet on X?', 'what does the data say?', 'is there edge?') and notes that agents using this tool convert better than those discovering packs separately. However, it does not provide explicit when-not-to-use instructions 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?

Annotations declare read-only, open-world, non-destructive. The description adds beyond: details on data sources (SEC EDGAR/XBRL for companies, FAERS for drugs) and return format (paired data + pipeworx:// citation URIs). 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 front-loaded with purpose, then usage triggers, then type-specific details. It is well-organized but slightly lengthy; could be more concise without losing 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?

No output schema, but description explains returned data fields per type and mentions citation URIs. It also notes performance benefit (replaces 8–15 calls). Adequate for the 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?

Schema coverage is 100%, so baseline is 3. Description reinforces schema with examples (e.g., 'tickers/CIKs' for company, 'names' for drug) but adds no new semantic depth beyond examples.

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 'Compare 2–5 companies (or drugs) side by side in one call', specifying the verb 'compare' and resource types. It distinguishes from siblings by noting it replaces 8–15 sequential calls, making it the dedicated comparison tool.

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 usage triggers are given: 'when a user says compare X and Y, X vs Y, how do X, Y, Z stack up, which is bigger, or wants tables/rankings'. It lacks explicit 'when not to use' or alternatives, but the context is clear.

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

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

Annotations declare readOnlyHint=true, openWorldHint=true, destructiveHint=false, but the description adds no behavioral context beyond that. It does not contradict annotations, but fails to elaborate on what operations are performed 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 word, which is under-specified. While concise, it lacks necessary detail to be useful. It does not follow best practices of front-loading key 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 high complexity (10 parameters, 1 required), no output schema, and no description of return values or behavior, the description is completely inadequate. It fails to provide essential context for correct tool 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?

Schema has 10 parameters with 0% description coverage. The description does not explain any parameter meaning, usage, or constraints (e.g., format of coordinates, allowed profile values). The description must compensate for low coverage but does not.

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 is 'Routing.', which is a tautology of the tool name. It fails to specify what the tool does, e.g., calculate directions between coordinates. This provides no more information than the name itself.

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?

No usage guidance is provided. The description does not indicate when to use this tool over siblings like 'directions_matrix' or 'map_matching'. The agent has no context for selection.

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, non-destructive behavior. The description adds no further behavioral context such as rate limits, result size constraints, or required permissions. 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 single short phrase is too minimal and lacks structure. While concise, it fails to convey essential information for a tool with multiple parameters; true conciseness would include critical details without excess.

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 (5 parameters, no output schema), the description is severely inadequate. It does not cover return value, error cases, or how coordinates should be formatted, leaving significant gaps for effective use.

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 5 parameters with 0% description coverage. The description does not explain any parameter's purpose, format, or relation to the matrix computation. An agent cannot infer parameter semantics from the given text.

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 'Distance/time matrix' which indicates the tool computes a matrix of distances and/or times, but it lacks specificity about inputs (e.g., between coordinate pairs) and does not distinguish it from sibling 'directions' which likely provides single-route directions.

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?

No guidance is provided on when to use this tool versus alternatives like 'directions' or 'map_matching'. The description does not mention prerequisites, use cases, or limitations.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safe, non-destructive nature is covered. The description adds that it returns 'the top-N most relevant tools with names + descriptions,' which is useful but does not elaborate on ordering or scoring. No contradictions.

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

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

The description is front-loaded with purpose and usage. The list of domains is comprehensive but slightly verbose. Overall well-structured and each sentence contributes value.

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

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

Given the tool's discovery function, the description covers what it does and what it returns (top-N tools with names+descriptions). No output schema is needed. The description is complete for an 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 coverage is 100% with descriptions for 'query' and 'limit'. The description provides natural language examples for query values (e.g., 'analyze housing market trends'), adding semantic 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: 'Find tools by describing the data or task.' It lists numerous specific data domains and explicitly distinguishes its use case from other tools by advising to 'call this FIRST when you have many tools available.'

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 instructs when to use: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear context and prioritization 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 readOnlyHint=true and destructiveHint=false. Description adds that results include pipeworx:// citation URIs. Could mention pagination or performance, but sufficient for a read-only aggregation tool.

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

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

Single, well-structured sentence that front-loads the purpose and flows naturally. No wasted words, all information earn their 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?

No output schema, but description explains return format (recent SEC filings, fundamentals, patents, news, LEI) and citation URIs. Covers both parameters and usage caveats. Complete for 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% with descriptions. Description adds clarification: type is only 'company' and value must be ticker or CIK (names not supported). 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 'Get everything about a company in one call' and lists specific data types returned (SEC filings, fundamentals, patents, news, LEI). Distinguishes from siblings by noting it replaces ten-plus pack tools.

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

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

Explicitly lists trigger phrases ('tell me about X', 'profile of Acme', etc.) and provides a condition for not using it (names not supported, use resolve_entity first). No 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 mark destructiveHint=true. Description confirms deletion and adds context about clearing sensitive data, but doesn't reveal additional behavioral details like error handling or persistence guarantees 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?

Three sentences, front-loaded with purpose, no wasted words. Ideal brevity for a simple 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?

Covers purpose, usage guidance, and sibling relations adequately for a simple destructive tool. Lacks mention of idempotency but is acceptable given low 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?

Schema coverage is 100% with detailed description for key. The description adds no extra meaning beyond repeating 'by key'. 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 the action (delete), resource (memory), and identifier (key). It differentiates from siblings by explicitly pairing with remember and recall, establishing its role in the memory lifecycle.

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 specific use cases (stale context, task completion, clearing sensitive data) and hints at alternatives by mentioning complementary tools. Lacks explicit when-not-to-use guidance but is useful 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?

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description adds no extra behavioral context such as coordinate system, accuracy, or rate limits. The arrow pattern implies transformation but lacks further disclosure.

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?

While extremely concise (3 words), it is under-specified for a tool with 9 parameters. Conciseness should not sacrifice necessary information. The description front-loads the core purpose but omits critical details.

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

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

Given no output schema and 9 parameters, the description is incomplete. It does not explain return values, coordinate system, or parameter usage. Sibling tools exist but are not contrasted, reducing 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?

With schema description coverage at 0%, the description should explain parameters. It does not mention any of the 9 parameters (e.g., query, bbox, limit, types), leaving them opaque. The single arrow phrase adds no value to parameter understanding.

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

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

The description 'Address → coords.' clearly indicates conversion from address to coordinates, which aligns with the tool name. However, it is vague about input format (e.g., street address, place name) and output specifics, and does not distinguish from sibling 'geocode_reverse' beyond direction.

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?

No guidance on when to use this tool versus alternatives. The sibling 'geocode_reverse' performs the inverse operation, but this is not mentioned, leaving the agent without context for selecting the correct 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 indicate readOnlyHint=true and destructiveHint=false, establishing it as a safe read operation. The description adds no further behavioral details such as rate limits, authentication requirements, or output characteristics.

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?

Although concise, the description is under-specified to the point of being unhelpful. It is a shorthand that sacrifices necessary detail for brevity, especially given the tool has 6 parameters.

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 tool with 6 parameters and no output schema, the description is grossly inadequate. It fails to explain optional parameters, output format, or any edge cases, leaving the agent with insufficient information for 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?

With 0% schema description coverage and no parameter explanations in the description, the agent has no understanding of what 'limit', 'types', 'country', or 'language' control. The description fails to compensate for the missing schema descriptions.

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

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

The description 'Coords → address' clearly conveys that the tool converts coordinates to an address. It implicitly distinguishes from the sibling 'geocode_forward' by stating the direction of conversion. However, it lacks specificity about the address format or scope.

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 no context on when to use this tool versus alternatives like geocode_forward or directions. No guidance on prerequisites or limitations.

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

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

The description adds no behavioral context beyond what annotations provide. Annotations indicate readOnlyHint=true, but the description does not confirm or expand on any traits, such as input limitations or output format.

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

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

The description is extremely concise (one word), but this is under-specification rather than effective conciseness. It does not earn its place by adding value.

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

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

Given the complexity of 8 parameters (including 'contours_colors' and 'contours_meters') and no output schema, the one-word description is completely inadequate for an AI agent to understand the tool's behavior and usage.

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

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

With only 13% schema description coverage (only the 'coordinates' parameter has a description), the tool description does not explain any of the 8 parameters. It fails to compensate for the low coverage, leaving parameter semantics entirely to 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 is simply 'Isochrones,' which is a tautology repeating the tool name. It does not use a specific verb to indicate the tool's action, such as 'compute' or 'generate,' leaving the purpose unclear.

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?

No guidance is provided on when to use this tool versus its siblings like 'directions' or 'map_matching.' The description lacks any context 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows the operation is safe and non-destructive. The description adds that it 'snaps' traces to roads, implying transformation of input coordinates to matched road paths. However, it does not disclose error handling, required point density, or whether the output is a new set of coordinates.

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?

At one sentence, it is extremely concise. However, it lacks detail; a slightly longer description with key parameter hints would improve utility without losing conciseness. Front-loading the core action is good, but the sentence stands alone with no additional structure.

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 9 parameters, no output schema, and no parameter descriptions, the description is insufficient. The agent cannot determine what the tool returns (e.g., matched coordinates, confidence scores, or route segment IDs) or how to use the optional parameters. A more complete description would explain the output and typical usage patterns.

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

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

With 0% schema parameter description coverage and 9 parameters, the description only mentions 'GPS trace', which likely maps to the required 'coordinates' parameter. It does not explain the purpose of other parameters like 'tidy', 'steps', 'profile', 'overview', 'radiuses', 'geometries', 'timestamps', or 'annotations', leaving the agent to guess their roles.

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 'Snap GPS trace to roads' clearly states the tool's action (snap) and resource (GPS trace to roads). It distinguishes from sibling tools like geocode (address to coordinates), directions (route planning), and isochrone (area reachable), as map matching is a specific process of aligning raw GPS points to road network.

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?

No guidance on when to use map matching versus alternatives like directions or geocoding. The description does not specify prerequisites (e.g., need for timestamps) or mention that input must be a sequence of coordinates in a specific order.

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?

Disclosures include rate limit (5/day), free usage, no quota impact, and that team reads digests daily affecting roadmap. Annotations (non-readOnly, non-destructive) are consistent. 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?

Well-structured: starts with purpose, then usage scenarios, then content guidelines. Sentences are mostly concise, though a bit lengthy. 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?

For a feedback tool with 3 parameters (all documented in schema) and no output schema, the description covers purpose, usage, content guidelines, and behavioral notes (rate limits, impact). Comprehensive and actionable.

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%, so baseline 3. Description adds value by explaining the 'type' enum in context (e.g., 'bug = something broke or returned wrong data') and advising on 'message' content (avoid user prompts, be specific).

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 defines the tool as feedback collection with specific intents (bug, feature, data_gap, praise). Distinguishes from siblings like ask_pipeworx and discover_tools by stating when to use it.

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 scenarios for use (bug, feature/data_gap, praise) and content guidelines (describe in terms of tools/packs, avoid pasting prompts). Mentions rate limits. Does not explicitly cover when not to use, but scenarios are well-covered.

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=true and destructiveHint=false. The description adds that the tool searches, groups markets, and returns ranked opportunities with reasoning. It does not mention potential rate limits or scope, but these are minor given the read-only nature.

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, front-loading the core purpose, then detailing modes in separate paragraphs. Every sentence adds essential information 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 the tool and no output schema, the description fully covers what the tool does, its two modes, why both modes are needed, and what it returns (ranked opportunities with direction and reasoning).

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

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

Schema coverage is 100%, so baseline is 3. The description adds significant value by defining each parameter's purpose, providing examples, and explaining the mode each parameter activates (event slug/URL vs. topic 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?

The description clearly states that the tool finds arbitrage opportunities by checking monotonicity violations across Polymarket markets, and distinguishes two modes: 'event' for single-event analysis and 'topic' for cross-event analysis. This differentiates it from siblings like 'validate_claim' and 'polymarket_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?

It explicitly explains when to use each mode: event mode for a single Polymarket event, topic mode for cross-event arbitrage. It also explains why topic mode is necessary (single-event mode misses cutoff-based opportunities), 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?

The description thoroughly explains the behavioral process: scanning top markets, grouping by asset, fetching price history once, computing model probability, and ranking by |edge|. This goes well beyond the annotations (readOnlyHint, openWorldHint, destructiveHint) and provides clear operational 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 moderately concise; it efficiently front-loads the main action and methodology, though slightly verbose. Each sentence contributes meaning, and the structure is logical, but it could be tightened slightly 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?

Despite lacking an output schema, the description explains the expected output (top N ranked by edge magnitude with suggested trade direction) and notes that V1 covers only crypto-price bets with a specific model. It omits edge cases like no edges found, but overall provides sufficient context for an agent to select and invoke the tool correctly.

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

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

With 100% schema coverage, the description adds value by specifying default values (limit=10, window=1wk, min_edge_pp=0.5), maximum limit (25), and clarifying that min_edge_pp is in percentage points. This aids 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 scans high-volume Polymarket markets and returns those where Pipeworx data disagrees with market price, specifically for crypto-price bets using a lognormal model. It distinguishes itself from siblings like polymarket_arbitrage by focusing on edge discovery.

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 frames the tool as solving the 'what should I bet on today' question, indicating its use for discovering opportunities without manual browsing. However, it does not explicitly mention when not to use it or provide alternative tools for different 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 provide readOnlyHint=true and destructiveHint=false. Description adds value by explaining scoping (anonymous IP, BYO key hash, account ID) and that it avoids re-deriving context, which is helpful 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?

Description is concise at ~80 words, front-loaded with the core action, but includes some extra context (scoping, pairing) that is beneficial. No wasted sentences.

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 no output schema and simple parameters, the description covers the retrieval behavior fully, including listing all keys and scoping. It provides sufficient context for an 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 coverage is 100% with a description for the key parameter. The description adds meaning by explaining the behavior when key is omitted (list all keys), which is not explicit in 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 the tool retrieves values saved via remember and lists all keys when key is omitted. It distinguishes itself from the sibling tools 'remember' and 'forget', establishing its role in the CRUD-like memory workflow.

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

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

Description explicitly tells when to use (to look up stored context) and when not (omit key to list all). It mentions pairing with 'remember' and 'forget', providing clear alternatives and context for usage.

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

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

Annotations indicate read-only and non-destructive behavior. The description adds details about parallel fan-out to SEC EDGAR, GDELT, and USPTO, return format (structured changes, count, URIs), and parameter formats. 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 four sentences, each carrying essential information: purpose, usage triggers, how it works, return details. No wasted words, well 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?

Despite no output schema, the description explains the return structure (structured changes, count, citation URIs). It also covers the tool's behavior (parallel sources) and parameter options, making it self-contained 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?

Input schema has 100% description coverage. The description adds useful context: explains 'since' format with examples, 'value' as ticker or CIK, and that 'type' is limited to 'company'. This goes beyond 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: 'What's new with a company in the last N days/months?' and provides specific example user queries. It also distinguishes from siblings by focusing on recency and changes, unlike static profile tools.

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

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

The description explicitly lists when to use the tool with example natural language queries. It does not mention when not to use or specific alternatives, but the context is clear.

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

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

Adds context beyond annotations: scoped by identifier, authenticated vs anonymous memory duration (24 hours). 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?

Single paragraph packed with info, front-loaded purpose, but could be slightly more structured (e.g., bullet points). Still concise.

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

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

Covers storage behavior, scoping, pairing, and persistence. Lacks return values and error cases, but for a simple storage tool it's fairly 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 already describes key and value with 100% coverage. Description adds naming conventions and notes that value is any text, enriching but not essential.

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

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

The description clearly states the tool saves data for reuse, with specific examples like resolved ticker, target address. It distinguishes from siblings recall and forget.

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

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

Explicitly states when to use: 'when you discover something worth carrying forward'. Also pairs with recall and forget, and clarifies scoping and persistence differences.

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

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

Annotations already provide readOnlyHint and openWorldHint, so the bar is lower. The description adds useful behavioral context: it returns 'IDs plus pipeworx:// citation URIs' and gives output examples. It does not contradict annotations. It could mention potential failure modes or result variability, but overall it is transparent enough.

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: two sentences plus examples. It front-loads the purpose and usage guidelines, and every sentence adds value. There is no unnecessary information, making it easy for an AI agent to parse quickly.

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 (two parameters, no output schema), the description is quite complete. It covers purpose, input examples, output format, and usage context. It could mention that results may be missing if not found (though openWorldHint implies this), but overall it provides sufficient context for 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?

Schema description coverage is 100%, so baseline is 3. The description adds value by providing examples that illustrate how to use the parameters (e.g., 'Apple' -> AAPL / CIK, 'Ozempic' -> RxCUI). These examples enhance understanding beyond the schema descriptions, justifying a higher 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?

The description clearly states the tool's purpose: 'Look up the canonical/official identifier for a company or drug.' It specifies the types of identifiers (CIK, ticker, RxCUI, LEI) and gives concrete examples. It distinguishes itself from sibling tools by indicating it replaces multiple lookup calls and should be used before other tools that require official identifiers.

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: 'Use when a user mentions a name and you need the CIK...' and 'Use this BEFORE calling other tools that need official identifiers.' It also notes that it replaces 2–3 lookup calls, implying it is a consolidated tool. However, it does not explicitly list alternatives or when not to use, which could be more comprehensive.

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=true and destructiveHint=false, so the safety profile is clear. However, the description adds no behavioral context beyond the bare label 'Static Image URL.' It does not disclose what the URL returns, how to use it, or any side effects (e.g., rate limits, caching). It adds minimal value 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?

At only two words, the description is under-specified rather than concise. It lacks structure such as a verb phrase, usage notes, or separation of concerns. Every sentence should earn its place; here there is only a noun phrase that does not convey meaning 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?

Given the tool's complexity (9 parameters, 6 required, no output schema), the description is completely inadequate. It does not mention return values, how to interpret parameters, or any limitations. An agent would have no idea what the returned URL represents or how it should be used.

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

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

With 0% schema description coverage, the description should explain parameters, but it provides no information about lat, lon, zoom, bearing, pitch, retina, or style_id. Parameter names are somewhat self-explanatory, but terms like 'bearing' and 'pitch' in a map context may require clarification. The description fails to compensate for the missing schema descriptions.

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

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

The description is a noun phrase ('Static Image URL.') rather than a verb+resource structure. It vaguely suggests generating a URL for a static image, but it does not clearly state the action the tool performs, such as 'Generate a static map image URL.' This is almost a tautology of the name, insufficient for distinguishing from sibling tools like directions or tilequery.

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?

No guidance is provided on when to use this tool versus alternatives like tilequery or directions. The description lacks any context about scenarios or prerequisites, leaving the agent to infer usage from the name alone.

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, but the description adds no further behavioral context such as error handling, performance, or limitations.

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?

Overly terse to the point of being uninformative. Conciseness should not sacrifice necessary detail.

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

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

With 8 parameters and no output schema, a single sentence is highly insufficient. The tool's purpose, behavior, and parameters are almost entirely unexplained.

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

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

Schema description coverage is 0%, and the description provides zero information about any of the 8 parameters, not even the 3 required ones.

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 is too vague: 'Tile feature query.' It does not specify what the tool does or distinguish it from sibling tools like 'directions' or 'isochrone'.

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?

No guidance on when or how to use the tool. No mention of prerequisites, context, or alternatives among the 21 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?

Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds that it extracts structured form, returns verdict with citation, and supports specific domains. It does not mention rate limits or error handling, but the openWorldHint suggests variability. A score of 4 is appropriate as it adds meaningful 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 concise and front-loaded: purpose, usage, scope, and output are covered in three sentences. No redundant information; every sentence adds value.

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

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

Given the simple input (1 parameter) and rich annotations, the description adequately covers purpose, usage, and output. However, it lacks detail on output schema (verdict types explained but no JSON structure), and could mention limitations like only US companies or only annual data. A score of 4 reflects minor gaps.

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

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

Schema description coverage is 100% and the description provides rich context for the single parameter: natural-language claim with examples and constraints (e.g., financial claims for US companies). This exceeds the baseline 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 uses specific verbs (fact-check, verify, validate) and explicitly states the resource (natural-language factual claim against authoritative sources). It distinguishes itself from siblings by noting it replaces 4–6 sequential calls, making its composite nature 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 explicitly states when to use the tool: 'when an agent needs to check whether something a user said is true' and provides example queries. It also specifies the scope ('company-financial claims for public US companies') and mentions alternatives are not needed because it consolidates multiple steps.

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