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

The description adds no behavioral details beyond the annotations already present (readOnlyHint, openWorldHint, destructiveHint). It fails to disclose what happens when required parameters are missing or how the open-world hint affects 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 extremely concise, but this comes at the cost of informativeness. While front-loaded, it is too minimal to be helpful.

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 three parameters, no output schema, and an open-world hint, the description fails to provide sufficient context for correct invocation. It does not describe the expected output or how parameters work together.

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

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

With 0% schema description coverage, the description must explain parameters but does not. 'Album metadata' gives no insight into the roles of artist, album, or mbid, leaving the agent uncertain about how to provide valid inputs.

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 is 'Album metadata,' which merely restates the tool name without specifying the action (e.g., 'get' or 'retrieve'). It does not clarify whether this tool reads, writes, or performs another operation, though annotations imply a read-only lookup.

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 siblings like artist_info or track_info. The description lacks any contextual cues about appropriate use cases.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description adds little behavioral context beyond stating the output content. It does not disclose rate limits, authentication needs, or side effects. The added value is minimal.

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 (six words) with no wasted text, but it is too minimal to be effective. It sacrifices informativeness for brevity, leaving critical gaps.

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

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

With no output schema and 0% parameter documentation, the description should compensate by explaining the return format and parameter roles. It only lists output types without structure or parameter guidance, making it incomplete 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 0%, meaning the scheme provides no parameter descriptions. The description does not explain the meaning or usage of any parameter (lang, mbid, artist), nor does it mention that 'artist' is required. It fails to add 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 'Artist bio + similar + tags.' clearly states the tool returns three types of information (biography, similar artists, tags), distinguishing it from sibling tools like artist_similar which likely only returns similar artists. The verb is implied but the resource is 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?

No guidance is provided on when to use this tool versus alternatives such as artist_similar or album_info. The description does not specify prerequisites, when to avoid, or which sibling tool to use for other needs.

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 already convey; it fails to explain how similarity is determined or any other behavioral traits.

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 short but this is underspecification rather than concise helpfulness; important information is missing.

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 3 parameters and no output schema, the description is completely inadequate and leaves critical gaps in understanding.

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

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

With 0% schema description coverage, the description should explain parameters but provides none; parameters like mbid, limit, and artist remain entirely unclear.

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 'Similar artists.' is a tautology that merely restates the tool name without specifying an action or distinguishing it from siblings.

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

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

No guidance is provided on when to use this tool versus alternatives like artist_info or artist_top_tracks.

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

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

Annotations already declare readOnlyHint, openWorldHint, destructiveHint. The description adds no extra behavioral context (e.g., pagination, rate limits, result limits) beyond what the annotations provide.

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

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

Single sentence, no filler, front-loaded. However, the extreme brevity limits the informative 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?

With three parameters, no schema descriptions, and no output schema, the description is insufficient. It does not cover parameter usage or result structure, making it incomplete.

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 0% and the description does not explain any parameters (mbid, limit, artist). The agent cannot distinguish between identifier parameters or understand the limit field.

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 'Most-played tracks for an artist.' clearly conveys the tool's output (tracks) and scope (an artist). It distinguishes from siblings like artist_info or artist_similar, though it uses an implicit verb.

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 versus alternatives, no when-not-to-use information. The agent must infer context from the tool 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 cover readOnly and open world; the description adds value by revealing internal routing across 2,520 tools and citation format, but could mention potential latency or 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?

Well-structured with critical instruction front-loaded, but slightly lengthy; could be trimmed without losing meaning while maintaining clarity.

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

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

Given single parameter and no output schema, the description sufficiently explains return format (structured answer with citations) and scope of questions; complete enough for its 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 a clear parameter description; the description enriches semantics by specifying the types of questions (e.g., 'what is', 'look up') and providing concrete 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 the tool routes questions to many tools/sources and returns structured answers with citations, explicitly saying 'PREFER OVER WEB SEARCH' and listing example queries and domains.

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

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

It provides explicit when-to-use guidance (factual questions about structured data) and implicitly when not (by contrasting with web search), along with example queries that cover the intended 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 provide readOnlyHint=true and destructiveHint=false. Description adds behavioral context: fans out to multiple data packs, classifies bet type, and returns an evidence packet with model comparison. This adds useful detail beyond annotations.

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

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

Description is dense but efficient, front-loading purpose and then detailing behavior. Every sentence adds value, though slight redundancy exists (e.g., mentioning 'core demo product' twice). Still concise for the amount of 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 moderate complexity (2 params, no output schema), the description covers input and behavior well but lacks detail on the return structure (evidence packet format, comparison output). This gap is notable without an output schema.

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

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

Schema coverage is 100%, baseline 3. Description adds meaning: explains 'depth' enum values ('quick = 2-3 sources, thorough = full fan-out') and default, and 'market' accepts slug, URL, or text. This significantly enhances schema understanding.

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

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

The description clearly states the tool's purpose: research a Polymarket bet by pulling Pipeworx data. It specifies input types (slug, URL, question text) and actions (resolve, classify, fan-out, return evidence). It distinguishes from sibling tools like polymarket_arbitrage by being the core research 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 use cases are given: 'should I bet on X?', 'what does the data say?', 'is there edge?'. It implies when to use it versus discovering packs individually, but does not explicitly state when not to use 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, openWorldHint=true, and destructiveHint=false. The description adds no behavioral context beyond what annotations provide. No mention of rate limits, data freshness, or any other traits. The description fails to contribute value beyond structured fields.

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 only three words, which is overly terse and not informative. While conciseness is valued, this under-specification does not define the tool's behavior. There is no front-loading of critical information; the description is effectively missing.

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 low complexity (1 optional parameter, no output schema), the description is still completely inadequate. It fails to specify what the tool returns (e.g., array of artist objects, structure), how the limit affects results, or any additional context like period or genre. Even with annotations, the description is too minimal for an agent to effectively use the tool.

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

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

There is one parameter 'limit' of type number with no description in the schema (0% schema description coverage). The tool description provides zero information about this parameter, such as what it controls (e.g., maximum number of artists) or its allowed range. The description must compensate for low schema coverage but fails entirely.

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 'Global top artists' is a noun phrase rather than a verb+resource statement. It vaguely indicates the tool returns a list of top artists but lacks a clear action verb like 'list' or 'get'. It does not differentiate from sibling tools such as 'tag_top_artists' or 'artist_info' which might return similar data.

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

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

No guidelines are provided about when to use this tool versus alternatives. Sibling 'tag_top_artists' suggests a narrower scope, but the description does not specify that this tool is for global charts or how it differs from general artist queries. The agent receives no context for tool 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?

Beyond annotations (readOnlyHint=true, destructiveHint=false), description reveals data sources (SEC EDGAR/XBRL for companies, FAERS for drugs) and return format (paired data with 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 information-dense but not overly verbose. Front-loads core purpose and type distinctions. Could be slightly trimmed, but all sentences are relevant.

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 adequately explains return value (paired data + citation URIs). Covers both entity types, includes efficiency note (replaces 8-15 agent calls). Complete for a comparison 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?

Adds significant meaning beyond the input schema: explains what each type retrieves (revenue, net income, cash, debt for companies; adverse events, approvals, trials for drugs) and value format (tickers/CIKs for company, names for drug). Schema coverage is 100%, but description enriches it substantially.

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 companies or drugs side by side, specifying verb ('compare'), resource ('companies or drugs'), and scope ('side by side'). It distinguishes from sibling tools like entity_profile which likely handles single entities.

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 ('compare X and Y', 'X vs Y', etc.) and describes use cases (tables/rankings of financials or drug events). Does not provide explicit when-not-to-use or alternatives, but context is clear enough.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description's main contribution is stating it returns top-N most relevant tools with names and descriptions. This adds clarity without contradicting annotations.

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

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

The description is a single paragraph that efficiently conveys purpose, examples, and usage guidance. While it could be more structured, it is not verbose and front-loads the key action.

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

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

Despite lacking an output schema, the description explicitly states what is returned (top-N tools with names and descriptions). Combined with strong annotations and schema coverage, the description fully contextualizes the tool 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?

Both parameters are fully described in the input schema (100% coverage). The description provides example queries but does not add significant meaning beyond the 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 starts with 'Find tools by describing the data or task' and enumerates many specific domains (SEC filings, FDA drugs, etc.), clearly distinguishing this discovery tool from the many sibling tools that perform specific tasks.

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 'Call this FIRST when you have many tools available and want to see the option set', providing clear when-to-use guidance and implying it's a prerequisite for selecting 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 already indicate read-only (readOnlyHint: true) and non-destructive (destructiveHint: false). The description adds significant behavioral context: it returns SEC filings, fundamentals, patents, news, and LEI, with 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?

The description is concise (4 sentences), front-loaded with purpose, and every sentence adds value. No redundant or missing 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?

Despite lacking an output schema, the description enumerates all return types (SEC filings, fundamentals, patents, news, LEI, citation URIs). It also notes limitations (person/place coming soon). This provides a complete picture for the agent.

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

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

Schema coverage is 100% (both parameters have descriptions). The description adds real-world guidance: 'Pass a ticker like "AAPL" or zero-padded CIK like "0000320193"' and clarifies the 'type' enum. This adds 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: 'Get everything about a company in one call.' It uses a specific verb ('Get') and resource ('everything about a company'), and distinguishes itself from siblings by noting it replaces multiple pack tool 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?

The description provides explicit usage examples (e.g., 'tell me about X', 'research Microsoft') and gives an exclusion: 'Names not supported — use resolve_entity first if you only have a name.' This clarifies when to use the tool versus an alternative.

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 destructiveHint=true. The description adds behavioral context by noting it clears sensitive data and pairing with remember/recall, which helps the agent understand the impact beyond the annotation. No contradiction.

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

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

The description is extremely concise – two sentences with no wasted words. The first sentence states the primary action, and the second provides usage guidance. It is front-loaded with the essential 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 low complexity (single parameter, no output schema, annotations present), the description is complete. It covers what the tool does, when to use it, and provides enough behavioral context for proper invocation.

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

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

The input schema has only one parameter with a full description ('Memory key to delete'), so schema coverage is 100%. The description does not add additional meaning beyond what the schema already provides, meeting the baseline.

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

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

The description clearly states the action ('Delete a previously stored memory by key') with a specific verb and resource. It also differentiates from siblings by naming 'remember' and 'recall', providing context within the memory tool family.

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: 'when context is stale, the task is done, or you want to clear sensitive data'. It also hints at when not to use by pairing with other tools, though not exhaustive. This provides clear context for usage.

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

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

Discloses key behavioral traits: rate-limited to 5 per identifier per day, free, doesn't count against tool-call quota, and that the team reads digests daily. Annotations provide some info but the description adds important context about impact and constraints.

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

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

The description is well-structured: starts with a one-line summary, then expands on when to use, then provides behavioral/constraint info. No redundant sentences; every sentence adds value without being overly verbose.

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

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

Given the tool's simplicity (no output schema, straightforward submission), the description covers all necessary aspects: purpose, parameter semantics, usage guidelines, and behavioral transparency. It leaves no ambiguity about what the tool does or how to use it 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?

The description adds meaning beyond the input schema by explaining the enum options (bug, feature, data_gap, praise) in detail, and provides guidance on writing the message (be specific, 1-2 sentences, 2000 chars max). For the context object, it describes optional fields and their usage.

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

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

The description clearly states the tool is for providing feedback (bugs, features, data gaps, praise) to the Pipeworx team. It distinguishes itself from sibling tools like 'ask_pipeworx' or 'discover_tools' by narrowing the scope to feedback types and advising against pasting user prompts.

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

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

Explicitly tells when to use the tool: 'Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise).' Also provides a clear 'don't' rule: 'don't paste the end-user's prompt.'

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 and destructiveHint=false; description adds internal behavior (walks child markets, groups related markets, checks monotonicity) and return format (ranked opportunities with trade direction+reasoning). 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 with clear heading for modes and examples. Sentences are efficient; no wasted words. Slightly verbose in explanation of cross-event mode but overall well-balanced.

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 adequately covers return value. Explains both modes thoroughly. Contextual signals (coverage, no required params) are consistent. No missing critical info for agent 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 100% with descriptions; description enriches parameters by explaining modes with examples (e.g., 'event slug or full URL', 'topic seed question'). 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 finds arbitrage opportunities via monotonicity violations. Distinguishes two modes ('event' and 'topic') with specific use cases, differentiating from sibling tools like '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?

Explicitly explains when to use each mode: 'event' for single event slug, 'topic' for cross-event search. Provides concrete example of when event mode fails and topic mode succeeds, guiding correct 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?

The description reveals behavioral traits beyond annotations: it's read-only (scans, returns), uses external data (FRED, coinpaprika), groups by asset, fetches price history once, computes model probability, and ranks by edge. This aligns with readOnlyHint=true and destructiveHint=false, adding valuable context like return of suggested trade direction.

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

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

The description is well-structured and front-loaded with the main action. It is slightly longer but each sentence adds value, explaining the model, data sources, and output. No extraneous information, though it could be more terse without losing clarity.

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

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

Given the complexity (model, multiple steps) and absence of output schema, the description adequately explains the workflow: scanning top markets, grouping, fetching price history, computing probability, ranking by edge, and returning top N with trade direction. It lacks details on error handling or edge cases but suffices for a read-only tool.

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

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

Input schema has 100% coverage with descriptions, so baseline is 3. The description reinforces default values and parameter roles (limit as top N, window as volume window, min_edge_pp as filter) but does not significantly expand beyond schema details. It adds minor context about ranking but maintains consistency.

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

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

The description explicitly states the tool's purpose: scanning high-volume Polymarket markets and returning those where Pipeworx data disagrees most with market price. It uses specific verbs (scan, return) and resources (Polymarket markets, Pipeworx data) and clearly distinguishes from siblings by focusing on edge magnitude ranking for opportunity 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 provides clear context for when to use the tool: it answers the 'what should I bet on today' question and saves users from manually paging through markets. It does not explicitly exclude alternatives like polymarket_arbitrage but implies its specific use case for crypto-price bets with model-based edge detection.

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, covering safety. Description adds scoping (anonymous IP, BYO key hash, etc.) and pairing info, but no additional behavioral traits beyond annotations.

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

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

Two sentences with no redundancy. Front-loaded with primary action, then usage context and sibling pairing. Every sentence adds value.

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

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

For a simple read-only tool with clear annotations and a single optional parameter, the description fully covers purpose, usage, and behavioral context without needing return value details.

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 100% coverage, so baseline is 3. Description adds meaning by explaining that omitting key lists all saved keys, and that retrieval is scoped to the agent's identifier.

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 uses specific verb 'retrieve' and resource 'value previously saved via remember', clearly distinguishing from siblings 'remember' and 'forget'. It also explains the list-all-keys behavior.

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

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

Explicitly states when to use: to look up context stored earlier without re-deriving. Mentions pairing with remember and forget, guiding the agent 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 annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: it fans out to three sources in parallel and returns structured changes with total_changes count and citation URIs. No contradiction with annotations.

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

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

The description is 5 sentences, well-structured with clear sections: purpose, use cases, sources, parameter details, and return format. It is informative without being verbose, though it could be slightly more concise by removing redundant phrasing.

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

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

Given no output schema, the description adequately explains the return format (structured changes, total_changes, citation URIs). It covers the parallel fan-out and parameter semantics. While it could mention potential limitations like rate limits or data freshness, the annotations (openWorldHint) imply the tool is non-deterministic, and the description is complete enough 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?

Schema description coverage is 100%, so baseline is 3. The description adds extra meaning: explains the 'since' parameter accepts ISO dates or relative shorthand (e.g., '7d', '30d') and suggests typical values like '30d' or '1m' for monitoring. This goes beyond the 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 starts with a clear purpose: 'What's new with a company in the last N days/months?' and provides example queries. It distinguishes from siblings by specifying the three data sources (SEC EDGAR, GDELT, USPTO) and the monitoring use case, setting it apart from other entity tools on the server.

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 example queries and states 'Use when a user asks...' or 'you're monitoring for changes.' This provides clear usage guidance. While it does not mention when not to use or alternative tools, the context is sufficient for typical agent 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?

Beyond annotations, the description adds scoping by identifier, persistence details (authenticated persistent, anonymous 24-hour), and storage format (key-value pair). This provides full transparency.

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

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

Single paragraph covering all needed aspects: purpose, usage, pairing, scoping, persistence. 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 2-parameter tool without output schema, the description covers all necessary context: purpose, when to use, pairing, scoping, persistence. 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 has 100% coverage with descriptions. The description adds naming conventions and examples for keys, adding context beyond the schema but not critically necessary.

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 across conversations/sessions, provides specific examples (resolved ticker, target address, user preference), and 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 says when to use (discover something worth carrying forward), mentions scoping by identifier, and pairs with recall/forget. Also differentiates persistence for authenticated vs anonymous sessions.

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

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

Annotations already mark the tool as readOnlyHint=true and openWorldHint=true. The description adds useful behavioral context: it returns IDs plus pipeworx:// citation URIs, and it lists specific identifier systems (CIK, ticker, RxCUI, LEI). There is no contradiction with annotations.

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

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

The description is concise (four sentences), well-structured, and front-loaded with the core purpose. Every sentence contributes essential information: purpose, usage context, examples, and guidance on ordering. 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 lack of an output schema, the description adequately explains what the tool returns (IDs and citation URIs). It covers input types and formats, usage sequence, and the scope (company/drug). This is comprehensive for a lookup tool with clear annotations.

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

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

Schema coverage is 100%, so the description's role is to add meaning. It provides concrete examples for the 'value' parameter (e.g., 'Apple' → AAPL) and clarifies the mapping of output IDs. This adds significant value beyond the schema's type/enum 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?

Description clearly identifies the tool's core function: looking up canonical/official identifiers (CIK, ticker, RxCUI, LEI) for companies or drugs. It uses specific verbs ('look up'), defines the resource ('canonical/official identifier'), and distinguishes itself from sibling tools by stating its role as a prerequisite for other tools.

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

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

The description explicitly states when to use the tool: when a user mentions a name and you need official identifiers required by other tools. It also advises to use it BEFORE other tools. However, it does not explicitly mention when not to use it or suggest alternative tools for similar tasks, which would earn a 5.

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

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

Annotations (readOnlyHint=true, openWorldHint=true, destructiveHint=false) already indicate a safe read operation. The description adds no behavioral context beyond the annotations, such as ordering, pagination, or what 'top' means. Since annotations carry the burden, the description adds minimal value.

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 sentence of four words, achieving maximal conciseness. It is front-loaded and contains 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?

There is no output schema, and the description does not explain the return format (e.g., artist names, IDs, popularity scores). For a tool with two parameters and no schema documentation, the description is insufficient for an agent to correctly interpret results.

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

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

The input schema has 0% description coverage, and the description does not mention any parameters. It fails to explain what the 'tag' parameter represents (e.g., genre name) or what the optional 'limit' does. The description adds no semantic meaning beyond the schema.

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

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

The description 'Top artists in a tag.' clearly indicates the tool returns top artists for a given tag. The verb 'top artists' implies a retrieve operation. However, it does not distinguish from the sibling tool 'chart_top_artists', which could have a different meaning (e.g., chart-based vs tag-based).

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 'chart_top_artists' or 'artist_info'. There is no mention of context, prerequisites, or exclusions.

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

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

Annotations already indicate read-only and non-destructive behavior, but the description adds no behavioral context (e.g., results format, possible 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 only two words, which is too brief to be useful. Under-specification is not conciseness.

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

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

No output schema and no description of return values. The tool has 3 parameters and sibling tools, yet the description fails to provide 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?

Input schema has 0% description coverage, and the description does not explain any parameter (e.g., what 'mbid' stands for or its role).

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 'Track metadata' is vague. It does not specify what metadata is returned or how it differs from sibling tools like 'track_search' or 'album_info'.

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, such as when to use 'mbid' versus 'artist'+'track', or when to prefer 'track_search'.

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 the annotations (readOnlyHint, openWorldHint, destructiveHint). It does not mention pagination, result format, or any other behavior expected from a search tool, leaving the agent without essential operational knowledge.

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 short, but this is not conciseness—it is under-specification. A single phrase without any structuring or prioritization of information makes it insufficient for an agent to understand the tool's purpose and usage.

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 search tool with two parameters and no output schema, the description is completely inadequate. It does not explain what a 'track' is, the search behavior (e.g., fuzzy matching, field targeting), or the expected return format, leaving the agent with insufficient information to use the tool correctly.

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

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

With 0% schema description coverage, the description must compensate but fails entirely. It provides no explanation of the 'query' or 'limit' parameters, their formats, or how they affect results. The schema alone gives type info but no semantic meaning.

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 'Track search.' is a minimal restatement of the tool name, lacking a specific verb or resource distinction. It does not clarify what kind of tracks or what the search returns, and fails to differentiate from siblings like 'track_info' which likely provides details on a specific track.

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 siblings such as 'artist_top_tracks', 'user_top_tracks', or 'tag_top_artists'. There is no mention of context, alternatives, or when not to use it.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the tool's safety is clear. However, the description adds no behavioral context beyond what the annotations provide—e.g., no mention of how 'top' is defined, time periods, or any other side effects. With annotations covering the safety profile, a score of 3 is appropriate.

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 an extremely short one-liner, but this is under-specification rather than conciseness. A good concise description would still convey core purpose and usage hints. Here, it is too brief to be helpful.

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 should indicate the return type or structure (e.g., 'Returns a list of track objects with rank'). It does not. For a tool with 3 parameters and no output schema, the description is grossly incomplete.

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

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

The schema has 0% description coverage for 3 parameters (user, limit, period), and the description 'User top tracks' adds no meaning to any parameter. For example, 'user' is required but not explained, and 'period' likely expects a time range but is left ambiguous. The description fails to compensate for the schema's lack of parameter 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 'User top tracks' is essentially a tautology of the tool name. It lacks a specific verb or resource distinction, such as 'Get the top tracks for a specified user.' Moreover, it fails to differentiate from sibling tools like 'track_search' or 'artist_top_tracks' that may also return tracks.

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 guidelines are provided. The description does not indicate when to use this tool versus alternatives like 'track_info' or 'artist_top_tracks', nor does it mention any prerequisites or exclusions.

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

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

Annotations already indicate read-only, non-destructive. Description adds detailed behavioral context: returns verdict, structured form, actual value with citation, percent delta, and underlying data sources (SEC EDGAR + XBRL). 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?

Concise and well-structured: purpose, usage, domain, return details, efficiency benefit. No wasted words; each sentence contributes.

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?

Even without an output schema, the description fully explains return verdicts and key output fields. For a single-parameter tool, it covers all necessary context.

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

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

Only one parameter with 100% schema coverage. The description provides example claims but does not add significant meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool's function: fact-checking and validating natural-language claims against authoritative sources. It provides specific use cases and example queries. It is distinct from siblings, which are mostly music and other unrelated 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 guides when to use (checking truth of user statements) with example phrasings. Also specifies domain (company-financial claims for v1) and notes efficiency gains by replacing multiple calls.

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