Postscript

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

Annotations already convey read-only, idempotent, open-world nature. Description adds meaningful context: external API usage (Anthropic), cost implication (BYO key), and return structure. No contradictions with annotations.

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

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

Three sentences front-load the core action, then provide key caveat and return format. No redundant words. Perfectly sized for quick comprehension.

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

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

Despite lacking an output schema, description fully explains return per-model and combined. Use-case list provides context. All necessary information is present for an agent to decide invocation.

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

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

Schema coverage is 100%, so baseline 3. Description enhances understanding by clarifying defaults (Workers AI Llama-3.3-70b), the role of '_apiKey', and the purpose of 'context' (disambiguation). Provides examples for entity and context.

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 primary action ('probe one or more LLMs') and outcome ('score visibility (0-100) per model'). It distinguishes from siblings like 'scan_competitor_ai_presence' by being more general. The verb+resource+outcome are specific and unambiguous.

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

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

Description provides explicit context: default free model, BYO key for Anthropic, and lists use cases (AI-marketing audits, brand checks, competitive monitoring). It does not explicitly state when not to use or name alternative tools, but the use-case list implies appropriate scenarios.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds key behaviors: routes to one of 3,272 tools, fills arguments, returns structured answer with stable pipeworx:// citation URIs. While not exhaustive on internal mechanisms, it adds valuable context beyond annotations.

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

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

The description is front-loaded with 'PREFER OVER WEB SEARCH' and uses a dense, informative style. While slightly verbose, each sentence earns its place by providing examples or expanding on tool capabilities. Could be trimmed slightly, but effectively structured.

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

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

Given no output schema, the description adequately explains return value (structured answer with citation URIs). It covers routing logic and provides plenty of examples. It lacks potential limitations (e.g., rate limits, unsupported queries), but overall is complete for an AI agent to decide when to 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?

Input schema covers 100% of parameters with descriptions for 'question' and its aliases (q, text, input, query, prompt). The description restates it accepts natural language but does not add new semantics beyond the schema. With full coverage, 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 routes factual questions to 3,272 tools across 728 sources, returns structured answers with citations, and distinguishes from sibling tools (e.g., entity_profile, recall). It provides specific use cases and examples, making the purpose unmistakable.

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

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

Explicitly says 'PREFER OVER WEB SEARCH' and lists when to use (SEC filings, FDA data, economic stats, etc.) with many examples. It also implies appropriate user queries like 'what is', 'look up', 'find', even if web search could answer. This provides clear context for selection.

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

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

Annotations already indicate safe read operation, but description adds critical behavioral details: low-confidence short-circuit, closed market handling, fallback attempts for news, and tradeability warnings. 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 lengthy but well-organized with clear sections (classifiers, fan-out examples, response shapes, safety). It front-loads the main purpose and uses headings for structure.

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

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

Despite no output schema, the description fully specifies response shapes, confidence levels, parent event extraction, and error conditions. It accounts for all scenarios an agent might encounter.

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

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

Schema covers all three parameters with descriptions; the tool description enriches with examples, defaults, and specific use cases for depth and include_raw, 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?

The description clearly states the tool's purpose: researching a Polymarket bet by pulling Pipeworx data, classifying, fanning out, and returning evidence. It gives specific usage context and distinguishes from related siblings by focusing on single bet research.

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

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

Explicitly says when to use ('should I bet on X', 'what does the data say', 'is there edge') and provides blocking conditions for low confidence or closed markets. Does not directly compare to siblings but implies appropriate contexts.

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 valuable behavioral context: it explains the data freshness fix ('post-Run-6 fix') and that results are sorted by primary metric. 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 the main purpose and contains multiple sentences that each add value. While slightly long, there is no fluff. Every sentence earns its place.

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

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

Given the tool's complexity (two entity types, multiple data sources), the description covers what data is pulled for each type and mentions the return format (paired data + citation URIs). Although no output schema exists, the description provides sufficient 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 coverage is 100%, so the schema already documents both parameters. The description adds meaning by explaining the type enum (company vs drug), providing examples for values (e.g., tickers vs drug names), and noting that results are sorted by primary metric. This goes 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 that the tool compares 2-5 companies or drugs side by side, specifies the data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs), and lists key metrics. This distinguishes it from sibling tools like entity_profile that likely handle individual 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?

The description explicitly says when to use: for 'compare X and Y', 'X vs Y', 'which is bigger', or rank-by-metric questions. It also states it replaces 8-15 sequential lookups. However, it does not explicitly state when not to use or mention alternatives, but the context is clear.

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

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

Annotations already mark it as read-only and idempotent. The description adds that it returns top-N relevant tools with full schemas and curated examples, ready for direct invocation. 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 few sentences with specific examples, but it could be slightly more concise. Still, it effectively communicates the purpose and usage without unnecessary 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?

Despite lacking an output schema, the description explains the return structure (top-N tools with names, descriptions, schemas) and notes that results are ready to call. Parameter count (6) is handled via aliases, and required parameter is clear.

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

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

Schema coverage is 100%, so baseline is 3. Description mentions the query parameter and aliases, but the schema already contains this information. Minimal added 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?

The description explicitly states 'Find tools by describing the data or task' and lists numerous domains. It clearly distinguishes itself from sibling tools by being a discovery tool that returns full schemas for direct calling.

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

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

The description provides explicit guidance: 'Call this FIRST when you have many tools available and want to see the option set'. This tells the agent exactly when and how to use it.

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

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

Annotations declare read-only and idempotent behavior, but the description adds rich behavioral details: specific return fields, data source notes, known issues (patents sunset, fundamentals fix), and input limitations. 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 verbose paragraph but front-loads the purpose and usage. While it includes detailed technical notes, every sentence adds value. Slightly long but not wasteful.

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

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

No output schema exists, so the description must explain return values thoroughly. It does so by listing fields, data sources, known limitations, and fixes. For a complex tool aggregating multiple APIs, the description is complete enough for an agent to invoke correctly.

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

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

Schema coverage is 100% with descriptions, but the description adds critical context beyond schema—type is only 'company' for now, value accepts ticker or zero-padded CIK, and names are not supported (use resolve_entity). This significantly enhances parameter understanding.

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

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

The description clearly states the tool aggregates everything about a US public company in one call, specifying 'Get everything about a US public company' as the verb+resource. It distinguishes from siblings by noting it replaces multiple pack tools, providing strong differentiation.

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 scenarios are given (e.g., 'tell me about X', 'research Acme', 'brief me on Tesla'). It also indicates when you would call this instead of multiple tools, but does not explicitly mention when not to use it or alternatives, which is a minor gap.

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 destructiveHint=true and idempotentHint=true. The description adds context about clearing sensitive data but does not disclose additional behavioral traits beyond what annotations provide.

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

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

Three sentences, each serving a distinct purpose: action, usage, and pairing. No redundant or unnecessary information.

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

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

For a simple delete operation with 1 parameter and annotations present, the description covers purpose, usage, and relationship to siblings 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% and the description mentions 'by key', consistent with the schema. No additional semantics are added, but the schema already thoroughly documents the parameter.

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

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

The description clearly states it deletes a memory by key using the verb 'Delete' and resource 'memory by key'. It distinguishes from siblings by mentioning 'remember' and 'recall' as paired 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 tells when to use the tool: 'when context is stale, the task is done, or you want to clear sensitive data'. It also provides alternatives by pairing with 'remember' and 'recall'.

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 the process (fetches page, extracts title/description/key links) and output format (standard llms.txt markdown). Annotations already indicate readOnly, idempotent, openWorld; description adds process detail 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?

Three focused sentences: purpose+why, process, use cases. Front-loaded with key information, no filler. Each sentence adds value.

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

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

Given the simple input (2 params, no output schema), the description covers purpose, process, output format, and use cases. No gaps for an agent to select and invoke correctly.

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

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

Schema coverage is 100% with detailed parameter descriptions. The tool description adds no extra parameter information, so baseline score of 3 is appropriate.

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

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

Explicitly states it generates an llms.txt file for a URL, specifying the verb and resource. Differentiates from siblings by focusing on AI indexability and specific output format. Use cases 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?

Lists three concrete use cases (client indexing, own project, competitor audit), providing clear context for when to use. Does not explicitly mention exclusions or alternatives, but use cases are sufficiently specific.

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 behavioral traits beyond annotations: rate-limited to 5 per identifier per day, free, does not count against tool-call quota, team reads digests daily. 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?

Concise, well-structured, front-loaded with core purpose. 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?

Complete for a feedback tool: covers what to write, how to structure, behavioral constraints. No output schema needed; context is fully addressed.

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

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

Schema already covers parameters with descriptions. The description adds value by explaining enum values in context and advising on message specificity, but the schema is already informative (100% 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's purpose: to tell the team about bugs, missing features, or praise, with specific categories. It distinguishes itself from sibling tools like ask_pipeworx or discover_tools by focusing on feedback.

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 when-to-use guidance (bug, feature/data_gap, praise), what not to do (don't paste end-user prompt), and additional constraints (rate limits, free, not counting against 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?

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds valuable context: data is self-aggregating from CF analytics-engine, no PII, cached 5min-1h, and returns (pack, tool, count) tuples. This significantly enhances understanding beyond annotations.

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

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

The description is concise and well-structured. The opening sentence states the purpose, followed by a bullet list of use cases and additional technical details. Every sentence adds value, and it's appropriately sized for the tool's simplicity.

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

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

For a simple read-only tool with one parameter and no output schema, the description is complete. It explains the output, use cases, data source, caching behavior, and privacy (no PII). 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?

The input schema already covers 100% of the single parameter 'window' with an enum and description. The description adds useful nuance: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' 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 that the tool returns top tools, top packs, and total call volume over a recent window. It specifies the resource (trending data on Pipeworx) and distinguishes it from sibling tools by focusing on aggregated trending 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 explicitly outlines three use cases: discovering hot data sources, confirming a canonical tool, and aligning use case with agent demand. While it doesn't explicitly say when not to use it or mention alternatives, the context is clear and actionable.

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, idempotent, non-destructive. Description adds rich behavior: partition sum check, Jaccard similarity filtering, placeholder detection, and response structure. No contradictions with annotations.

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

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

Well-structured with bold headers for modes and filters, front-loaded with core purpose. Though lengthy, the complexity of arbotrage detection justifies detail; slight verbosity but no wasted sentences.

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

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

Covers all aspects: input modes, internal logic (similarity threshold, partition filter), output fields (gap_pp, suggestions, reasoning), and error handling (rejected pairs, null signals). No output schema, so description fully compensates.

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; the tool description adds mode-specific context (event for single, topic for cross-event) and clarifies topic usage (searches related events, runs comparator on union). Adds meaning beyond schema.

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

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

Description clearly states tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. Two modes are explicitly defined with distinct use cases (event slug vs. topic search), distinguishing 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?

States when to use each mode (single event vs. cross-event) with examples. Does not explicitly exclude alternatives, but context from sibling names implies specialization. Could be clearer on when not to use, but adequate for guided selection.

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

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

Annotations already indicate readOnlyHint, idempotentHint, and non-destructive. The description adds substantial behavioral details: caching at KV level keyed on all knobs with 1h refresh, explanation of three segments (model_driven, structural_arbitrage, concentrated_longshot), and diagnostics for debugging empty results. 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 dense but front-loaded with the core purpose. Every sentence adds value, but the length (several paragraphs) could be slightly more structured. For a complex tool with 9 parameters and 3 segments, the conciseness is appropriate, but a shorter version might be possible.

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

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

Given the tool's complexity (9 parameters, three output segments, no output schema), the description is remarkably complete. It explains the output structure (by_segment, fed_candidates, _diagnostics), lists fields like edge_pp_net, kelly_fraction, and market details, and warns about Fed bets. It addresses why segments might be empty via diagnostics, ensuring agents understand results.

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

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

All 9 parameters have schema descriptions (100% coverage). The description adds value beyond schema by explaining the knobs in context: e.g., slippage_pp notes 'Polymarket has zero trading fees as of 2024 but bid/ask + thin depth typically eats 20-50bp per trade.' min_partition_leg_kelly is explained in relation to partition arbs. This provides meaningful guidance beyond bare schema.

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

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

The description clearly states the tool's purpose: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It specifies the action (scan), resource (Polymarket markets), and unique value (Pipeworx disagreement). It distinguishes from sibling tools like polymarket_arbitrage by focusing on Pipeworx data disagreement rather than general 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 provides explicit usage context: 'Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets.' It explains the three segments and when they apply. However, it doesn't explicitly state when NOT to use this tool versus alternatives like polymarket_arbitrage or ask_pipeworx, 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 read-only, idempotent, etc. The description adds rich behavioral context: how bet shape mismatches and temporal misalignment affect results, and what safety fields indicate. 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 verbose with technical details on safety fields. While front-loaded with purpose, it could be more concise. Some sentences are dense but essential for 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, the description thoroughly explains the response structure, leg-by-leg prices, spread, and safety fields including compatibility_warning and temporal_alignment. It covers edge cases and return values 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 descriptions. The description adds value by explaining the two modes and how parameters interact (topic vs explicit override), going beyond mere schema documentation.

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, using specific verb+resource. It distinguishes from sibling tools like polymarket_arbitrage by its cross-venue focus.

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

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

The description explains two modes (topic and explicit) and includes safety fields like compatibility_warning, guiding when spreads are meaningful. It warns that pre-mapped topics often return warnings, but does not explicitly compare to sibling tools.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds value by revealing return fields: keyword text, status, and configuration (welcome message, list assignment), which is useful without an output schema. 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 two sentences long, front-loading the action and resource, then listing return values. There is no extraneous information, making it highly 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?

For a simple retrieval tool with full schema coverage and clear annotations, the description is complete. It covers the retrieval action, key identifier, and return fields, leaving no significant 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 input schema covers both parameters with 100% description coverage. The description does not add additional meaning beyond the schema, so the baseline score of 3 is appropriate.

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

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

The description clearly states the tool retrieves a single SMS keyword by ID, specifying the verb 'Get' and the resource. It distinguishes from sibling tools like postscript_list_keywords by focusing on a single ID-based lookup.

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

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

The description implies usage when you have a keyword ID, but it does not explicitly state when to use this tool versus alternatives like postscript_list_keywords. No exclusions or alternative recommendations are provided.

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 confirm non-destructive, read-only, idempotent behavior. The description adds value by detailing the returned fields (phone, email, status, etc.), providing useful 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?

Single, well-structured sentence that front-loads the purpose and lists key return fields. No redundant information; every word serves a 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?

Despite lacking an output schema, the description explicitly lists all significant return fields (phone, email, status, Shopify linkage, custom properties), providing complete contextual information for an agent to understand the tool's output.

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

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

Schema coverage is 100% and both parameters have descriptions. The description does not add additional meaning beyond what the schema already provides, so baseline score applies.

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 ('Get'), the resource ('single SMS subscriber'), and the identifier ('by Postscript subscriber ID'). It also lists the specific return fields, distinguishing it from sibling tools like postscript_list_subscribers.

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

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

The description implies usage for fetching a single known subscriber, contrasting with list tools. However, it lacks explicit guidance on when not to use or alternatives, though the context makes it 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 provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint, which inform safety and idempotency. Description adds that it returns specific fields (keyword IDs, text, settings), but no further behavioral details (e.g., pagination, sorting). Since annotations carry most burden, description adds moderate 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, no filler. First sentence states main action, second explains keywords and return values. Well front-loaded and 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 explains return contents (keyword IDs, text, settings). For a simple list tool with comprehensive annotations, this is complete. No gaps identified.

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% (parameter `_apiKey` is described in schema). Description does not mention parameters, but schema already provides sufficient meaning. Baseline score of 3 is appropriate.

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

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

Description clearly states it lists all active SMS keywords, explains what keywords are (text-to-join words), and specifies return values (keyword IDs, text, subscription settings). This distinguishes from sibling tools like postscript_get_keyword (single keyword) and postscript_list_subscribers (different resource).

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

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

No explicit when-to-use or when-not-to-use guidance, nor alternatives mentioned. However, the purpose is clear enough that an agent can infer to use this for listing all keywords, and use postscript_get_keyword for a specific keyword. Lack of explicit differentiation reduces score.

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, and idempotentHint=true. The description adds value by detailing pagination behavior but does not elaborate on other traits like rate limits or authorization beyond what the schema covers.

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

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

The description is extremely concise: two sentences pack the purpose, capabilities, and pagination details without any superfluous content. Every sentence earns its place.

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

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

Given 11 parameters, 100% schema coverage, and rich annotations, the description adequately covers pagination and response shape. It does not explain defaults or empty results, but is sufficient for usage. No output schema exists, but the description compensates.

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 each parameter is well-documented. The description provides a high-level summary of filterable fields (email, phone, shopify_customer_id, timestamps) and sorting, which adds marginal value over the schema alone.

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

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

The description clearly states the tool lists SMS subscribers for a Postscript Shopify shop, using the verb 'list' and specifying the resource and context. It distinguishes from sibling tools like postscript_get_subscriber and postscript_list_keywords.

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 outlines filtering, sorting, and pagination capabilities, providing clear usage guidance. While it lacks explicit 'when not to use' or alternative tool references, the sibling set is sufficiently differentiated.

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, idempotent, non-destructive; description adds scoping and pairing with remember/forget, 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?

Three concise sentences with clear purpose and examples, no redundancies.

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 retrieval behavior, scoping, and pairing with other tools; adequate for a simple tool without output schema.

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

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

Single parameter; schema coverage is 100% and description adds no extra meaning beyond the schema property 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?

Clearly states it retrieves a value by key or lists all keys, distinguishing from siblings 'remember' and 'forget'.

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

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

Provides examples of when to use (retrieve stored context) and mentions scoping to identifier, but no explicit exclusions.

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

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

Annotations already declare readOnlyHint, openWorldHint, etc. Description goes beyond by detailing parallel fan-out to three sources, fallback logic (GDELT→GNews), and sunset status of USPTO API. 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?

Well-structured: purpose first, then use cases, then fan-out details, then parameter clarification, then alternative. No wasted sentences; each earns its place.

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

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

Despite no output schema, description fully explains return structure (changes[] grouped by source, total_changes count, citation URIs). Covers fallback, sunset, and parameter nuances. 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 coverage is 100% but description adds extra meaning: explains 'since' accepts ISO dates or relative shorthand with examples, and 'value' can be ticker or CIK. This surpasses the schema descriptions.

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

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

Description clearly states 'What's new with a company in the last N days/months?' and provides explicit use cases ('what's happening with X', 'updates on Y', etc.). It distinguishes from sibling tool 'entity_profile' by specifying when to use the 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?

Provides explicit when-to-use examples and a clear when-not-to-use with an alternative: 'Use entity_profile instead when you want the static profile...' This leaves no ambiguity.

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

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

Discloses key behavioral traits: key-value storage scoped by agent identifier, persistence differences between authenticated and anonymous users, and idempotency (implied by 'save' and annotations). Adds context beyond annotations (retention policies, scoping). 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 pack all essential info without fluff: purpose, usage scenario, key-value details, scoping, retention, and pairing with sibling tools. Front-loaded with the main action, each sentence earns its place.

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

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

For a simple tool (2 string params, no output schema), description fully covers what an agent needs: what to store, when, how it's stored (scope, retention), and how it relates to other tools (recall, forget). 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 already documents both parameters with 100% coverage, giving baseline 3. Description adds value with concrete examples for key (e.g., 'subject_property') and clarifies value as 'any text — findings, addresses, preferences, notes'—going 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?

Description starts with 'Save data the agent will need to reuse later'—a clear verb+resource. It explicitly distinguishes from sibling tools 'recall' and 'forget' by naming them and explaining pairing. Scope (across conversation/sessions) and key-value structure are stated, making purpose unambiguous.

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

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

Explicitly states when to use: 'when you discover something worth carrying forward' with concrete examples (ticker, address, preference). Provides motivation ('so you don't have to look it up again') and distinguishes between authenticated (persistent) and anonymous (24-hour) sessions. Pairs with recall and forget, guiding selection among siblings.

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

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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. Description adds that each call cascades through several lookup endpoints internally, replacing 2-3 manual lookups. This provides 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 concise and well-structured. Starts with main purpose, followed by usage guideline, then details on each supported type. No redundant sentences; every sentence adds value.

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

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

Despite no output schema, description fully compensates by detailing return values for each entity type. Complete enough for an agent to know exactly what the tool returns and how to interpret results.

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

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

Schema has 100% coverage with descriptions for both parameters. Description adds significant extra meaning: for 'company' returns ticker, CIK, company_name, citation URI; for 'drug' returns RxCUI, ingredient, brand, citation; notes auto-disambiguation and accepts multiple input formats.

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 resolves user-spoken names to canonical/official identifiers. Provides specific verb and resource (resolve entity), supported types (company, drug), and distinguishes from sibling tools by being the ID resolution 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 FIRST when you have a name but need an ID.' Provides clear context for when to use, but does not explicitly mention when not to use or list alternative tools. However, the guidance is strong and 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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. Description adds that it probes entities, ranks by score, and returns a ranked list with score, confidence, signal density. This adds some behavioral context but does not disclose potential rate limits or API dependencies beyond the optional Anthropic key.

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

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

Three sentences, concise and front-loaded with the main purpose. Each sentence adds value: purpose, method, use case. No redundant information.

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

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

Tool has 4 parameters (1 required), no output schema. Description explains return format (ranked list with score/confidence/signal density) and references sibling tool. The complexity is moderate and description covers the essential behavioral and usage aspects fully.

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

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

Schema descriptions cover 100% of parameters. The description adds critical context: first entity is treated as 'subject' for narrative, and clarifies model support and API key requirement. This enriches schema information.

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 compares AI visibility across entities side-by-side, uses ai_visibility_check, and ranks results. It differentiates from siblings like ai_visibility_check (single entity) and compare_entities (general comparison) by focusing on AI-specific competitive audits.

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 an explicit use case with example ('does Claude know about us as well as our competitors?'), indicating when to use. Lacks explicit exclusions or handling of edge cases, but the context sufficiently guides selection.

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

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

Annotations already signal safe, idempotent, read-only behavior. Description adds significant behavioral details: fans out across two sources, partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed lists timeouts. 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?

One dense sentence front-loading the purpose, followed by outputs, ecosystem constraints, and edge cases. While packed, every clause adds value. Slightly long but not wasteful.

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

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

No output schema exists, so description fully explains return value structure (summary block, per-advisory detail, links, alternative versions). Also covers ecosystem scope and partial failure behavior, 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%, so baseline is 3. Description adds extra context: scoped packages (e.g. '@types/node') accepted, version defaults to latest when omitted. This clarifies parameter handling 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 a specific verb-resource combination: a composite check for adding an npm package. It lists concrete outputs (license, advisories, bundle size, etc.) and distinguishes from siblings by noting NPM-only scope and alternative tools 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?

Explicit usage context: 'whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also specifies when not to use (NPM only, other ecosystems via deps.dev:version) and mentions partial failures and graceful degradation.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds that it uses SEC EDGAR + XBRL for financial claims, returns verdicts with structured data and citations, and replaces multiple sequential calls. This provides useful context beyond the structured 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, well-organized paragraph that front-loads the main action, followed by usage guidance, scope, return details, and value proposition. 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 no output schema, the description adequately explains return values (verdict, structured form, actual value with citation, percent delta) and context (company-financial claims via SEC). It lacks coverage of error scenarios or non-financial claims but is sufficient for the intended 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?

With 100% schema coverage, the description does not add significant meaning beyond the schema's description of the 'claim' parameter. The schema already includes examples. The description is sufficient but does not enhance parameter understanding beyond baseline expectations.

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 fact-checks factual claims against authoritative sources, provides example usage patterns, and specifies the scope (v1: company-financial claims). It effectively distinguishes itself as a specialized fact-checker, not just a general Q&A 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?

The description explicitly says to use when checking if something a user said is true and gives example queries. However, it does not provide explicit when-not-to-use guidance or compare with sibling tools like ask_pipeworx or bet_research, leaving some ambiguity.

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