Balldontlie

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

Annotations already indicate readOnly, idempotent, non-destructive. The description adds important context: default model is free, Anthropic requires BYO key and direct payment, and output includes per-model details. 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 a single paragraph of about 80 words, front-loaded with core function, then adding model details, output, and use cases. Every sentence adds value without redundancy.

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

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

Despite no output schema, the description explains the return shape (per-model score, confidence, signals, raw_response) and combined view. It covers all parameters, cost implications, and use cases, making it complete for the tool's complexity.

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

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

Schema coverage is 100%, and the description adds practical meaning: 'entity' is the thing to ask about with examples, 'models' lists supported values and default behavior, '_apiKey' explains when needed and pass-through nature, 'context' aids disambiguation. This goes beyond mere schema 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 it probes LLMs for knowledge about a business/brand/product/topic and scores visibility (0-100) per model. It specifies the default model and optional Anthropic probing, making the tool's function specific and distinct from siblings.

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

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

The description provides clear use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It does not explicitly state when not to use or alternatives, but the context and sibling list indicate no direct alternative, so the guidance is sufficient.

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

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

Annotations already provide readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds that it routes questions to appropriate sources, fills arguments, and returns stable pipeworx:// citation URIs, which goes beyond annotations by explaining the internal routing and output format. 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 well-structured, starting with the most important instruction ('PREFER OVER WEB SEARCH') and providing clear usage guidance. While slightly long, every sentence adds value. Could be slightly more concise, but the trade-off for completeness is acceptable.

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

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

Given the tool's complexity (routing to many sources), no output schema, and rich annotations, the description is remarkably complete. It covers purpose, usage, examples, behavioral details, and alternatives. No gaps for an agent to correctly invoke and understand 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?

Input schema has 100% coverage with descriptions for all parameters (including aliases). The description adds examples of valid questions but does not detail parameter semantics beyond what the schema provides. Baseline 3 is appropriate given full schema coverage.

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

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

The description clearly states the tool routes questions to 4,774 tools across 1242 sources and returns structured answers with citations. It distinguishes itself from sibling tools like ask_pipeworx_grounded (single answer with evidence) and deep_research (broad multi-part questions), and lists specific data domains it covers.

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

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

Explicitly instructs to prefer over web search, lists when to use (e.g., 'what is', 'look up', 'current') with concrete examples. Clearly states when to step up to other tools: ask_pipeworx_grounded for hallucination-resistant single answer, deep_research for broad/multi-part questions. Also mentions it works on every tier and is the default entry point.

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

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is clear. The description adds valuable context about refusal mechanisms (with enumerated reasons), evidence extraction, and cost tradeoffs, going beyond annotations.

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

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

The description is fairly detailed but every sentence adds value. It is front-loaded with the core purpose and structured logically. Could be slightly more concise 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 (hallucination resistance, refusal reasons, cost), the description fully covers behavior, return format, and use cases. No output schema exists, but the description details the return structure completely.

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

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

Schema coverage is 100% with all parameters described. The description does not add significant meaning beyond noting that the question is in natural language and that it uses the same routing as ask_pipeworx. 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 it is a 'hallucination-resistant answer mode for high-stakes reads' and specifies it extracts answers using only tool results, returning structured data with evidence. It distinguishes itself from the sibling ask_pipeworx for casual lookups.

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

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

Explicitly states when to use (when answering must be factual, e.g., financial verdicts, legal claims) and when not to (prefer ask_pipeworx for casual lookups due to extra cost). Provides clear guidance and alternatives.

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

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

Annotations (readOnlyHint, idempotentHint, etc.) are fully consistent and the description adds extensive behavioral detail: fan-out patterns per category, resolver contract with match confidence, parent event extraction, news fallback handling, safety block on low confidence, closed market handling, wide spread warning, and resolution-rule risk. 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 long but well-structured with section headers (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.) and front-loaded with the core purpose. Every sentence provides unique value; however, some detail (e.g., specific FRED series IDs) could be streamlined. Still, for the complexity, it earns its length.

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

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

Given the tool has no output schema, the description thoroughly covers return shapes (result.market, result.analysis, result.evidence), resolver contract, parent event extraction, news fallback, safety behaviors, and edge cases. It leaves no gap for an agent to misuse the tool, even addressing resolution-rule risk.

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

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

With 100% schema description coverage, the description still adds value: explains the three forms of 'market' input (slug, URL, question text), defines 'depth' enum values with behavior, and clarifies 'include_raw' default and when to use true (e.g., recompute deltas, cite observations). Examples in schema reinforce semantics.

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 a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies inputs (slug, URL, question text) and outputs (evidence packet + market-vs-model comparison). It distinguishes from siblings like ask_pipeworx (general Q&A) and deep_research (unguided) by being specific to Polymarket bet analysis with category-specific fan-outs.

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 cases: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' Also implies when not to use by detailing low-confidence and closed-market blocking behaviors. However, it doesn't explicitly contrast with sibling tools like polymarket_edges or validate_claim, leaving some ambiguity.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds significant behavioral context: data sources (SEC EDGAR/XBRL, FAERS), specific metrics returned (revenue, net income, adverse event counts), sorting by primary metric, 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?

The description is well-structured with a front-loaded purpose, but it is somewhat verbose. However, every sentence adds value, and it is not excessively long.

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 (two entity types, multiple data sources, sorting, citations), the description covers key aspects. No output schema exists, but the return format is mentioned. Minor omissions like pagination or limits are acceptable for this use case.

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

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

Schema coverage is 100% with descriptions for both parameters. The description goes beyond by explaining the semantics for each type (e.g., for company: 'pulls LATEST 10-K revenue + net income + cash + long-term debt') and providing examples (tickers for company, drug names). It adds substantive 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 uses specific verbs like 'compare', 'rank', 'head-to-head' and clearly states the resource: side-by-side comparison of 2–5 companies or drugs. It distinguishes from siblings by explicitly preferring this over sequential single-pack lookups, and the sibling list includes entity_profile, a likely alternative.

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 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It provides example queries and explains what data each type pulls. It does not explicitly state when not to use it, but the context is clear enough.

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

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

Beyond annotations (readOnly, idempotent, openWorld), the description adds crucial behavioral details: account requirement, paid plan for 'thorough' depth, expectation of 15-60s response time, explicit gaps[] for unanswered facets, and that it never invents information. No contradictions with annotations.

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

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

The description is well-structured with critical info front-loaded (account requirement, alternatives). It is slightly verbose but each sentence adds value. 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?

Given the tool's complexity (2 params, no output schema), the description fully explains the return format (findings packet, citations, gaps), expected behavior (parallel decomposition, routing to 4774 tools), and caveats (news topics yield empty gaps). No missing critical 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?

Parameters are fully covered in schema. Description adds value by explaining enum values (quick=3, standard=5, thorough=8) and clarifying that the question parameter accepts broad, natural language input. This enhances understanding beyond the schema.

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

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

The description clearly states the tool performs grounded multi-source research across 1242 structured data sources, contrasting with open-web search and asking_pipeworx for single lookups. It specifies the output as a findings packet with evidence, confidence, and citations, distinguishing it from sibling tools.

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

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

Explicitly provides when to use (broad/multi-part questions over structured data) and when not to use (breaking news, single lookups). Clearly states alternatives like ask_pipeworx and news-specific tools, and notes account requirements.

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, idempotent=true, destructiveHint=false. Description adds that it returns top-N most relevant tools with full schemas and curated examples, ready to call directly. No contradictions; complements annotations well.

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

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

The description is somewhat long but well-structured: purpose first, then usage guidance, then examples. Every sentence adds value, though could be slightly more concise without losing meaning.

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, the description adequately explains what is returned (names, descriptions, schemas). It covers the key behavioral aspects. Sufficient for an agent to understand the tool's role.

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. The description adds context that the query parameter accepts natural language and aliases, reinforcing usability. While baseline 3, the extra clarity on natural language input justifies a 4.

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

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

The description clearly states the verb ('Find', 'discover') and resource ('tools'), specifying it returns tools by describing data or task. It distinguishes this from sibling tools like 'search_within' by focusing on tool discovery rather than data searching.

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 'Call this FIRST when you have many tools available and want to see the option set.' Lists common use cases. Does not explicitly state when not to use, but the guidance is clear and helpful.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive. The description adds context: fans out across multiple sources, mentions USPTO soft-fails until reactivated, lists return fields. 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 example queries and provides rich detail. While slightly long, every sentence is informative. Could be tightened slightly but still efficient for the complexity.

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, description thoroughly explains return fields (CIK, filings with URIs, fundamentals sorted, patents, news, LEI) and covers edge cases like USPTO deprecation and name handling. Complete for a complex tool.

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

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

Schema has 100% description coverage. Description adds value beyond schema: provides real-world examples (ticker 'AAPL', CIK '0000320193'), clarifies that only 'company' type is supported, and reiterates name restriction. Enhances understanding.

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

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

Description uses specific verbs ('full cross-source profile'), names the resource ('US public company'), and distinguishes from siblings like resolve_entity by mentioning name resolution limitation. The examples ('Tell me about X', 'research Acme') further clarify purpose.

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

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

Explicitly states when to prefer this tool ('ALWAYS PREFER over chaining single-pack lookups when user asks for holistic view') and when not to ('names not supported – use resolve_entity first'). Clear decision criteria.

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

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

Description says 'Delete', matching destructiveHint=true. No contradiction with annotations. Adds context about clearing sensitive data, which goes beyond annotations.

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

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

Two sentences: first defines the action, second gives usage guidance. No wasted words, front-loaded with core purpose.

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

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

With one required parameter, no output schema, and annotations present, the description is complete. It covers purpose, usage, and context relative to siblings.

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

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

Schema has 100% coverage, description of parameter is minimal but sufficient. The tool description does not add extra meaning beyond what the schema provides.

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

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

Clearly states the tool deletes a memory by key, with a specific verb and resource. Distinguishes from sibling tools like 'remember' and 'recall' by implying deletion.

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: stale context, task done, clear sensitive data. Also mentions pairing with remember and recall, providing clear usage context.

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

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

Annotations (readOnly, idempotent, not destructive) cover safety. Description adds context on the data returned (specific fields). No contradictions. Lacks mention of potential null values or error cases, but acceptable for a simple lookup.

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 that is front-loaded with the verb and resource, followed by a clear usage instruction. No unnecessary words.

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

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

For a simple lookup tool with one parameter and an output schema, the description covers purpose, usage, and key fields. No missing information needed for an AI agent to invoke correctly.

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

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

Only one parameter 'id' with 0% schema description coverage. Description implies it is the game ID but does not explicitly define its format, range, or source. The example in schema helps somewhat but description could be more explicit.

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 a single NBA game by ID and lists the fields returned (matchup, scores, date, period, time, venue, season, postseason flag). This distinguishes it from sibling 'games' which likely returns a list.

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 'Use after games() to fetch detail', providing clear guidance on when to use this tool versus the sibling tool for listing games.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. Description adds that it is filtered and returns matchup/scores. No contradiction. Could mention pagination via cursor, but acceptable.

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, no redundant information. Front-loaded with key filtering and return info. 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?

With 6 parameters, no required, and output schema present, the description covers main functionality and use cases. It omits pagination details, but output schema likely covers return structure. Sufficient for an agent.

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

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

Schema description coverage is only 17% (only 'dates' has format hint). Description explains filtering by season, team, date range, which partially compensates. However, parameters like cursor, per_page, postseason are not elaborated, so 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 clearly states it filters NBA games by season, team, or date range and returns specific fields. It distinguishes from sibling tools like 'game' (singular) and other entity-specific tools like 'player', 'team'.

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

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

Provides explicit use cases ('did the Lakers win last night', schedule lookup, historical game search). Does not explicitly state when not to use or mention alternatives like 'stats' or 'season_averages', but the examples are 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 indicate readOnlyHint, idempotentHint, and non-destructive. The description adds beyond this: fetches the page, extracts title/description/key links, and emits standard llms.txt markdown. 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 three sentences: purpose, process, use cases. Front-loaded with purpose, every sentence adds value, no wasted words.

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

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

With only 2 parameters and no output schema, the description explains the output as a single text blob ready for site-root/llms.txt. It covers core functionality and output format completely for this simple tool.

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

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

Schema description coverage is 100% for both parameters (url and max_links). The description does not add significant meaning beyond what the schema already provides; it mentions 'max 50' but that's in 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 generates a production-ready llms.txt file for any URL, with a specific verb 'generate', resource 'llms.txt', and target 'any URL'. It distinguishes from siblings like ai_visibility_check by focusing solely on llms.txt generation.

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

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

The description explicitly lists three use cases: getting a client's site indexed, drafting for own project, auditing competitor. It provides clear context but does not explicitly state when not to use or directly compare to 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, destructiveHint=false, idempotentHint=true. The description adds value by specifying the return fields (id, type, params, created_at, last_fired_at, fire_count) and implying default filtering to active subscriptions. 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?

Two sentences, each earning its place. First sentence states purpose and output, second gives usage advice. No unnecessary words.

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

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

Given low complexity (1 optional param, no output schema but return fields listed), annotations covering safety, and clear usage guidance, the description is complete and well-suited for an AI agent.

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

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

Schema coverage is 100% with a clear description for the single parameter (include_inactive). The description doesn't add significant meaning beyond the schema, baseline of 3 is appropriate.

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

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

The description clearly states 'List the caller's active subscriptions' with a specific verb and resource. It distinguishes from sibling tools like 'subscribe' and 'unsubscribe' by focusing on listing.

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

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

Provides explicit guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' Clearly states when to use, though doesn't explicitly list when not to use.

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

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

Discloses rate-limiting (5 per identifier per day), free status, and that team reads digests daily. Annotations are all false (no read-only, no idempotency), and the description accurately reflects the tool's behavior without contradiction.

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

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

The description is concise, with three sentences front-loaded with the purpose. Every sentence adds value—usage guidelines, behavioral notes, and parameter tips—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 tool has 3 parameters (including a nested object) and no output schema, the description covers all necessary context: what feedback is, how to structure it, rate limits, and team processing. No gaps remain.

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 for all parameters. The description adds value by elaborating on how to write the message (specific, 1-2 sentences, 2000 char max) and explaining the 'type' enum values further. Context object usage is clarified.

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

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

The description clearly states the tool's purpose: sending feedback to the Pipeworx team about bugs, missing features, or praise. It distinguishes from sibling tools by specifying feedback types and usage 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?

Explicitly lists when to use (bug, feature, data_gap, praise) and provides guidance on what not to do (don't paste end-user prompt). Also mentions rate limit and that it's free, not affecting quota.

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, idempotentHint, destructiveHint), the description adds that the data is self-aggregating, derived from CF analytics-engine, contains no PII, and is cached 5min-1h depending on window. This provides valuable behavioral context.

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

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

The description is concise and well-structured: a clear opening statement, followed by bullet-pointed use cases, and ending with additional context. Every sentence adds value without redundancy.

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

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

Given the simple parameter set and no output schema, the description thoroughly covers what the tool returns, the parameter options, use cases, data source, privacy, and caching behavior. It leaves no major gaps.

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

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

The description adds meaning to the 'window' parameter beyond the schema's enum values, explaining that shorter windows surface what's hot now and longer windows show steady-state demand. This helps the agent choose appropriately.

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 precisely states what the tool does: returns top tools, top packs, and total call volume over a recent window. It differentiates from sibling tools like ask_pipeworx and discover_tools by focusing on aggregated trending data.

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

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

The description lists three specific use cases (discovering hot data, confirming canonical tools, aligning use cases). However, it does not explicitly mention when not to use the tool or provide 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, idempotentHint=true, and destructiveHint=false. The description adds specific field names but no additional behavioral traits beyond what annotations imply. Consistent 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 extremely concise: one sentence plus a directive. No wasted words; front-loaded with purpose and usage guidance.

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 parameter, simple output schema), the description covers purpose, usage sequence, and return fields. Annotations provide further safety guidance. Near-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?

With 0% schema description coverage and a single 'id' parameter, the description only says 'by ID' without explaining format, range, or source of the ID. This is insufficient for such a minimal schema.

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

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

The description clearly states it retrieves a single NBA player by ID, listing specific fields (name, position, height, weight, jersey, team). This distinguishes it from sibling tools like 'players' which returns a list.

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

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

The description advises 'Use after players() to confirm,' providing clear usage context. It implies a sequence but does not explicitly cover 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 and destructiveHint=false, so the safety profile is clear. The description goes beyond annotations by stating the search/filter behavior and listing returned fields, which adds value 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 two sentences long, front-loading the core purpose and return fields, then adding a specific use case. Every sentence is informative and 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?

With 4 parameters and an output schema, the description covers purpose, usage, return fields, and a key use case. It does not mention pagination (cursor, per_page) or that the result is a list, but the output schema likely covers that. Overall adequate for a search 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 low at 25% (only query has a description). The description partially compensates by explaining query (name substring) and team_ids (filter by team), but it does not describe cursor or per_page parameters, leaving them unexplained.

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 searches an NBA player roster by name substring or filters by team, and specifies it returns ID and various player attributes. It distinguishes this from the sibling 'player' tool by noting its use for resolving names to IDs for stats queries.

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

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

The description explicitly says when to use this tool: to search by name substring or filter by team. It also provides a clear use case: resolving a player name to a numeric player_id required by stats/season_averages, which implies not to use it if you already have an ID.

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

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

Adds substantial behavioral context beyond annotations: explains internal logic (semantic anchor, partition filter, fill check), response structure, and caution about trading. No contradiction with readOnlyHint and other 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?

Well-structured: starts with purpose, then explains each mode, then details filters and response. Although long, the information is densely packed and front-loaded, making it efficient for an AI agent.

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

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

Despite no output schema, the description fully explains the response structure, fill check logic, and integrates with sibling tools. Covers all behavioral aspects needed for correct selection and 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?

Both parameters have schema descriptions (100% coverage). Description adds extra meaning with examples and clarification on accepted formats (URLs for event, seed questions for topic), justifying a score above 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 clearly states the tool's purpose: finding arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes between three modes (no args, event, topic) and provides specific details, differentiating it from siblings 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?

Explicit guidance on when to use each mode (no args, event, topic) and when not to trade based on fill check. Mentions polymarket_fill_risk for custom sizing. Lacks explicit exclusions but provides strong contextual cues.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds significant behavioral context: caching (1h at KV level), edge calculation methods (lognormal barrier, GDELT volume ratio, partition overround with per-sport bias correction), diagnostic output (_diagnostics), and detailed response structure (by_segment, fed_candidates). It explains why Fed bets are excluded and how empty segments arise, going well beyond annotation coverage.

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 long but well-structured, starting with purpose and then detailing each segment and knob. It contains essential detail for a complex tool, but some redundancy exists (e.g., repeated explanation of knobs in multiple places). Front-loading the purpose helps agents quickly grasp the tool's function.

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 9 parameters fully documented, no required parameters, and no output schema, the description fully compensates by detailing the response structure (top-level by_segment, fed_candidates, _diagnostics), caching behavior, and edge calculation rationale. It also explains filter skips and why segments might be empty, providing complete context for an AI agent to invoke and interpret results correctly.

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

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

Schema coverage is 100%, providing baseline 3. The description adds rich semantics: for each parameter it explains default, purpose, and practical usage tips. For example, max_spread_pp says 'Set to 2 to require tight books', min_liquidity describes avoiding thin-book opportunities, and min_partition_leg_kelly clarifies that partition arbs have zero parent-level Kelly and this knob applies per-leg. This significantly aids correct parameter selection.

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 Polymarket markets to find opportunities where Pipeworx data disagrees with market price, with a specific use case 'what should I bet on today'. It details three distinct segments (model_driven, structural_arbitrage, concentrated_longshot), making it easy to distinguish from sibling tools like polymarket_arbitrage or polymarket_kalshi_spread.

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 extensive guidance on when to use, including tradeable-edge knobs (min_liquidity, max_spread_pp), how to interpret empty segments via diagnostics, and exclusion of Fed bets. However, it lacks explicit comparison to siblings like polymarket_arbitrage for when one should prefer this tool over alternatives, leaving some ambiguity.

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

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

The description adds extensive behavioral context beyond annotations: it uses daily snapshots, has a 60-day TTL, decay from daily closes (not intraday), and explains snapshot gaps. This aligns with readOnlyHint, openWorldHint, and idempotentHint without contradiction.

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

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

The description is detailed but well-structured with sections for args, response fields, and limits. It is front-loaded with the core purpose. Minor lengthiness is justified by the complexity; 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?

Without an output schema, the description fully explains the three response arrays (tracked, expired, snapshot_dates) with field meanings, trends, and limits. It also covers data boundaries and what is not provided (intraday), making it complete 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 description adds meaning beyond the schema: default values for days (14), clamp range (2-30), window options with defaults, and explains how parameters affect the response. The schema coverage is 100%, but the description enhances understanding significantly.

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: providing edge persistence and decay telemetry from daily snapshots. It answers a specific question about how long an edge has existed, distinguishing it from sibling tools like polymarket_edges which likely provide current edge values.

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 tool explains when to use it (to assess edge durability and decay) and contrasts fresh vs old wide edges. It implies alternatives (polymarket_edges for current edges) but does not explicitly state when not to use it or exclude other 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 (readOnlyHint, idempotentHint, destructiveHint) are complemented by the description detailing the ladder walking, return fields (top_of_book, vwap_fill_price, verdict), and risks like forced_directional_risk. This adds significant behavioral context beyond the annotations.

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

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

Though lengthy, the description is well-structured with clear sections (REQUIRES, SINGLE-MARKET, BASKET) and each sentence adds value. It is front-loaded with the main purpose and avoids redundancy.

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

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

Given the tool's complexity (two modes, detailed return fields, behavioral warnings) and absence of an output schema, the description is remarkably complete. It covers parameter interpretation, return values, and risk scenarios, leaving no critical gaps.

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

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

Schema description coverage is 100%, and the description adds meaning by explaining how size_usd is interpreted differently (spend vs target proceeds vs settlement notional) for each mode, and how side defaults to auto in basket mode based on partition sum.

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

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

The description clearly states it performs a 'realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes between single-market and basket modes, each with specific outputs. It explicitly differentiates from sibling tools like polymarket_arbitrage and polymarket_edges by advising usage before acting on their signals.

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 conditions: 'REQUIRES one of market or event', and directs usage 'before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It warns against thin books and partial basket fills, which helps the agent decide when to use the 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?

The description adds significant behavioral context beyond annotations: it explains that the tool compares two venues, details compatibility_warning scenarios (non-equivalent bet shapes, semantic mismatch), temporal alignment constraints, and skipped cross counters. Annotations already indicate readOnly and idempotent, but description enriches understanding.

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

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

The description is relatively long but every sentence adds value. It is structured with clear sections (TWO MODES, RESPONSE, SAFETY FIELDS) and is front-loaded with the core purpose. A minor reduction in length could be possible 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?

Despite no output schema, the description fully explains the response format (leg-by-leg prices, top_spreads_pp) and safety fields (compatibility_warning, temporal_alignment, skipped counters). It covers all necessary context for an AI agent to understand and 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?

Schema coverage is 100%, so the schema already documents parameters. However, the description adds valuable context about the interaction between parameters (topic overrides? explicit overrides topic-mapped side) and provides examples. It clarifies the two-mode logic beyond the schema's descriptions.

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

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

The description clearly states the tool computes 'Cross-venue spread between Kalshi and Polymarket for the same resolving question'. It uses specific verbs and resources, and distinguishes from sibling tools like polymarket_arbitrage which focuses on intra-venue arbitrage.

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

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

The description explicitly explains two modes (topic shortcuts vs explicit tickers), when to use each, and provides extensive guidance on interpreting safety fields (compatibility_warning, temporal_alignment, skipped_cross_type). It tells when spreads are meaningful and when they aren't.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds scoping (anonymous IP, BYO key hash, or account ID) and behavior when key omitted (list all keys), exceeding annotation 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?

Two sentences, front-loaded with primary function, no unnecessary words. Every sentence serves a purpose.

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

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

For a simple tool with one optional parameter and comprehensive annotations, description covers retrieval, listing, scoping, and pairing with related tools. No gaps.

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

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

Schema coverage is 100% with parameter description. Description reinforces meaning of key and adds dual behavior (retrieve vs list), adding 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 specifies verb (retrieve/list) and resource (saved values/keys), distinguishes from siblings (remember, forget) by naming them explicitly.

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

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

States when to use (look up stored context without re-deriving) and pairs with remember and forget, providing clear usage 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 indicate read-only, destructive false, idempotent, and open-world. The description adds value by detailing return data (source, citation_uri, payload) and explaining the 'mark_read' behavior for state management, which goes 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 exceptionally concise and well-structured, front-loading the core purpose. Every sentence adds value without redundancy, and the total length is appropriate for the tool's complexity.

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 5 parameters and no output schema, the description sufficiently explains return fields and parameter behaviors. The mention of an alternative feed adds completeness, though it could briefly mention pagination or limit behavior.

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

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

With 100% schema coverage, baseline is 3. The description supplements schemas by providing examples ('sec_8k') and clarifying that 'mark_read' affects subsequent calls, thus adding contextual meaning beyond the schema definitions.

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

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

The description clearly states the tool retrieves recent alerts from a subscription feed, with specific return fields. It does not explicitly distinguish from sibling tools like 'list_subscriptions', but the unique focus on 'fired events' sets it apart.

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

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

The description advises that polling works and mentions an alternative REST endpoint, providing context on when to use the tool versus a script. It also explains the effect of 'mark_read' for iterative calls, though it lacks explicit when-not-to-use guidance.

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

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

Beyond annotations (readOnly, openWorld, idempotent), the description discloses that it fans out to multiple sources, includes fallback logic, and soft-fails for USPTO. It also states the return format (changes grouped by source, total_changes count, 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 a single paragraph that is information-dense but clear. Every sentence adds value, though slightly more structured formatting could improve readability.

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 adequately specifies the return format. It covers all input parameters, behavioral details (sources, fallback), and usage context, making it well-rounded.

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

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

Schema coverage is 100%, and the description adds significant meaning: explains type only supports 'company', gives examples for value (ticker or CIK), and provides guidance for since (e.g., '30d' for typical monitoring).

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 provides a change feed for a company, fanning out to multiple sources (SEC EDGAR, GDELT/GNews, USPTO) for a specified time window. It distinguishes from sibling tool entity_profile by advising when to use each.

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

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

The description provides clear usage examples (e.g., 'What's new with X'), explains when to use this tool versus entity_profile, and details the since parameter with typical values. It also mentions fallback behavior for news and USPTO soft-failure.

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 idempotentHint: true and destructiveHint: false. The description adds key behavioral details: scoped by identifier, persistent for authenticated users, 24-hour retention for anonymous, and pairing with recall/forget. 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?

Four sentences, front-loaded with purpose. Every sentence adds value: usage context, storage details, pairing instructions. 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 tool with two string params and no output schema, the description covers all necessary context: what, when, how to store, scope, persistence, and how to retrieve/delete. No gaps.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds examples of keys and values (e.g., 'subject_property', 'target_ticker') and states 'any text', providing context 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 purpose: 'Save data the agent will need to reuse later' and gives specific examples (resolved ticker, target address, user preference). It distinguishes itself from siblings recall and forget by mentioning pairing with them.

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

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

Provides clear usage context: 'Use when you discover something worth carrying forward' and gives examples. Implicitly tells when not to use by mentioning alternatives (recall, forget). Does not explicitly exclude other scenarios but is sufficient.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. Description adds that each call cascades through several lookup endpoints, providing useful behavioral context beyond annotations.

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

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

Description is well-structured with examples front-loaded, but slightly verbose. Every sentence adds value; could be trimmed slightly but still efficient.

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

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

Despite no output schema, description fully explains return values per type (including citation URIs), internal cascading behavior, and parameter usage. Complete for a 2-param lookup 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%, but description adds meaning: explains what each type returns (ticker+CIK+company_name for company, RxCUI+ingredient+brand for drug) and gives example values, surpassing 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?

Description clearly states the tool resolves names to official identifiers, with specific examples (ticker, CIK, RxCUI). It distinguishes itself as the first step when needing an ID, and lists supported types with details.

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

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

Explicitly says 'Use FIRST whenever you have a name but need an ID' and mentions it replaces 2-3 manual lookups. No exclusion criteria for when not to use, but context is clear.

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

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

The description adds significant behavioral context beyond annotations: it explains the internal call to ai_visibility_check, ranking by score, and output fields (score, confidence, signal density). Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false, and the description complements these without contradiction.

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

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

The description is a single, well-structured paragraph. It is front-loaded with the main action, then provides details on internal mechanics, use case example, and output. Every sentence serves a purpose.

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

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

Despite no output schema, the description adequately describes return value (ranked list with score, confidence, signal density). It covers purpose, usage, parameters, and behavior comprehensively for a competitive audit tool.

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

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

With 100% schema coverage, baseline is 3. The description adds critical semantic value: entities param's first entry treated as 'subject' for narrative, context param disambiguates common names, models param explains defaults and API key requirement. This goes well beyond schema descriptions.

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

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

The description uses a specific verb+resource: 'Compare AI visibility across multiple entities side-by-side.' It distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (general comparison) by focusing on AI visibility and ranking.

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: 'Useful for competitive AI-marketing audits' and gives an example question. However, it does not explicitly mention when not to use or list alternatives like compare_entities, leaving some ambiguity.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds behavioral details: partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, sources_failed lists timeouts. No contradiction.

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

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

Single paragraph with dense information, but each sentence contributes meaning. Could be more structured (e.g., bullet points), but it is clear and efficiently written without extraneous content.

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

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

No output schema, but description details return structure (summary block fields, per-advisory details, links, alternative versions) and handles partial failures. Covers parameter behavior and usage context thoroughly for a two-parameter tool.

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

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

Schema coverage is 100% and includes descriptions for both parameters. Description adds extra context: package accepts scoped packages (e.g., '@types/node'), version defaults to latest. 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 it is a composite check for npm packages using deps.dev and bundlephobia, with specific verb 'scan' and resource 'dependency'. It distinguishes from siblings by noting NPM-only focus in v1 and alternative for other ecosystems.

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

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

Explicitly says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. It also specifies when not to use (other ecosystems) and provides alternative (deps.dev:version directly).

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

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

Annotations declare safe read-only and idempotent traits. Description adds detailed behavioral traits: returns top-N passages with offsets and scores, uses BGE-base-en embeddings with cosine similarity over 500-char overlapping windows, and has a 200K char cap with truncation flag. 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?

Description is detailed but each sentence serves a purpose. Front-loaded with core function. Slightly long but efficient for the richness 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?

No output schema, but description compensates by explaining return format (top-N passages with offsets and scores) and technical details (embedding model, window size, truncation). Covers all usage aspects comprehensively.

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 repeats schema info for 'text' and 'query' but adds minimal new meaning (e.g., 'max ~200K chars' already in schema). Does not significantly enhance understanding beyond schema.

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

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

The description clearly states 'Semantic search INSIDE a fetched record' with a specific verb and resource. It distinguishes from siblings by contrasting with 'ask_pipeworx_grounded' and explaining when to use each.

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 ('when the record is too big to cram into the prompt') and mentions an alternative approach ('Pairs with ask_pipeworx_grounded'). Provides actionable 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, idempotentHint=true, and destructiveHint=false. The description adds value by specifying the exact return data (PTS, REB, AST, FG%, etc.) and clarifying that it operates per player per season, going beyond the annotations without contradicting them.

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, front-loaded sentence with no redundant words. It efficiently states the action, inputs, and outputs in a clear order: 'Fetch per-season NBA stat averages for one or more player_ids in a given season year; returns PTS, REB, AST, FG%, and other averages per player.'

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

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

Given the tool's simplicity (2 required params, no nested objects) and presence of an output schema, the description covers core functionality well. It omits details like pagination or error handling, but for a straightforward fetch operation, it is nearly complete. The listing of specific stats adds completeness.

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

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

Schema description coverage is 0%, so the description must compensate. It clarifies that 'season' is a 'season year' and 'player_ids' are 'one or more' player IDs. This adds basic meaning but lacks details like format or expected ranges. It is minimally sufficient but not rich.

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

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

The description uses a specific verb 'Fetch' and clearly identifies the resource 'per-season NBA stat averages' for given player_ids and season year. It lists exact stat categories (PTS, REB, AST, FG%), which distinguishes it from sibling tools like 'player' or 'stats' that cover broader or different data.

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

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

The description implies usage when per-season averages for specific players are needed but does not explicitly state when to use this tool over alternatives. No guidance on when not to use it or mention of sibling tools like 'player' or 'stats' that might serve similar purposes.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds context by listing returned stats and filtering dimensions, which is useful but not critical.

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

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

Two sentences: first specifies purpose and filters, second lists returned stats and use cases. Efficient and front-loaded without fluff.

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

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

Output schema exists, so return values are already defined. Description covers stats and filters but does not mention pagination or postseason. With 0 required params and examples, it is mostly 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 description coverage is 0% for parameters. Description mentions high-level filters (player, team, game, season) which map to player_ids, game_ids, seasons but not cursor, per_page, postseason. Partially compensates but lacks parameter-level details.

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

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

Description clearly states it returns per-game NBA player box-score stats filtered by player, team, game, or season. It lists specific stats (PTS, REB, etc.), distinguishing it from sibling tools like 'player' or 'game'.

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 states use cases: individual game performance lookup, box-score analysis, fantasy stats. It does not mention 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?

Adds substantial context beyond annotations: account requirement, phone verification, SMS cap, webhook auto-disable, delivery channel behaviors. No contradiction with annotations (readOnlyHint false, idempotentHint true consistent).

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 with clear structure, examples, and operational details. Slightly verbose but all information earns its place; could use bullet points for readability.

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 types, parameters, delivery options, account requirements. No output schema, but mentions returns subscription id. Lacks only clarifications on idempotency behavior, which annotations already hint.

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, baseline is 3, but description adds rich examples for each type's params and delivery constraints, significantly aiding correct parameter construction.

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

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

Clearly states it creates a proactive monitoring subscription to a live-data event stream and returns the new subscription ID. Differentiates from sibling tools like unsubscribe and list_subscriptions.

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

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

Provides when to use (proactive monitoring, with account requirement) and what it covers (supported types, delivery channels). Could be improved by explicitly contrasting with pull-based alternatives like recent_alerts.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint as true and destructiveHint false. The description adds that it returns example questions from a live catalog with tool+argument shapes, which is useful behavioral context beyond annotations.

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

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

The description is relatively long but every sentence is informative. It is front-loaded with common user questions and structured with a clear purpose. Could be slightly more concise, but no redundancy.

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

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

Given the tool's role as a discovery entry point, the description thoroughly covers what it does, when to use it, parameter options, and the nature of the return value (category-bucketed example questions). No output schema exists, but the description sufficiently explains outputs.

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

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

Schema coverage is 100% for the single optional 'topic' parameter. The description adds value by listing example values (finance, pharma, etc.) and explaining that omitting topic gives a cross-category spread, which is not in the schema.

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

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

The description clearly states the tool is the onboarding entry point for learning what Pipeworx can do, returning category-bucketed example questions with exact tool+argument shapes. It distinguishes itself from siblings like ask_pipeworx and discover_tools by being the first-use discovery 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?

Explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' Also provides guidance on calling with no arguments for full spread or passing a topic to focus. This is excellent usage context.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc., covering safety. The description adds what fields are returned, but does not elaborate on error cases or prerequisites. With annotations present, this is adequate.

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?

A single, well-structured sentence that front-loads the core purpose and adds a usage hint. 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 simple tool (one parameter, annotations present, output schema exists), the description fully covers what an agent needs: purpose and usage context.

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

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

Schema coverage is 0% with no parameter description. The description only says 'by ID' without explaining the source or format of the ID. For a simple numeric parameter, more explicit guidance (e.g., 'from teams()') would improve 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 retrieves a single NBA team by ID, listing specific fields (name, abbreviation, city, conference, division). It distinguishes itself from the sibling 'teams' tool by noting its use for confirmation.

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

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

The description explicitly says to use this tool 'after teams() to confirm details,' providing clear sequential guidance. It does not list when not to use, but the context of siblings makes alternatives clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds value by specifying the exact fields returned and the purpose of the output (resolving team_id). No contradictions with annotations.

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

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

Two sentences: first lists the returned data, second explains usage. Front-loaded with critical information. No unnecessary words.

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

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

Given the tool's simplicity (no parameters, read-only, idempotent), the description fully covers purpose, content, and usage rationale. Output schema handles the rest.

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?

Tool has zero parameters, so description properly focuses on behavior and output. Schema coverage is trivially 100%. Baseline for 0-parameter tools is 4.

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

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

Description explicitly states 'All 30 NBA teams' with fields ID, name, city, abbreviation, conference, division. It distinguishes itself from sibling tools by explaining its role as a directory to resolve team names into numeric IDs required by 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?

Description provides clear usage context: 'Use as a directory to resolve a team name into the numeric team_id required by other balldontlie tools.' While it doesn't explicitly exclude alternative uses, it gives strong guidance for when to invoke it.

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

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

Adds beyond annotations: ownership enforcement, deactivation (not deletion), and connection to recent_alerts for historical events. 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?

Two efficient sentences, front-loaded, 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?

Complete for a simple mutation tool; covers ownership, data modification behavior, and output context via recent_alerts.

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%; description restates that id is from subscribe but adds no new meaning beyond the schema description.

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

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

The description clearly states the action (cancel), the resource (subscription by id), and adds ownership enforcement and deactivation vs deletion, distinguishing it from subscribe and list_subscriptions.

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

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

States when to use (cancel a subscription) but does not explicitly exclude alternatives or provide when-not-to-use guidance; however, the context with siblings makes usage clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds that it returns a verdict with structured details and a citation, providing context beyond annotations. 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 examples and domain-specific details. It is slightly lengthy but every sentence adds value; could be trimmed slightly without loss.

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 single parameter and no output schema, the description is complete. It covers purpose, usage, return type, and limitations (company-financial claims only).

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% for the single parameter 'claim'. The description adds examples of valid claim formats, but no additional semantic depth beyond what the schema provides.

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: natural-language claim verification against authoritative sources. It specifies the domain (company-financial claims) and the sources (SEC EDGAR + XBRL), distinguishing it from general fact-checking 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 advises using it when the agent needs to check factual correctness, and mentions that it replaces multiple sequential calls. It could be improved by explicitly stating when not to use it, but the domain restriction is clear.

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