Easypost

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

Beyond annotations (readOnlyHint, etc.), the description reveals that it calls external LLMs, costs nothing for Workers AI but requires a BYO API key for Anthropic, and returns per-model data plus a combined view. 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 three sentences, each adding value. It front-loads the core action (probe and score visibility) and efficiently explains parameters and use cases. 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?

The tool has 4 params, 1 required, and no output schema. The description explains the return format (per-model {score, confidence, signals, raw_response}) and combined view. It could detail 'signals' further, but overall it equips an agent to understand inputs and outputs sufficiently for 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?

Schema coverage is 100%, so baseline is 3. The description adds practical context: default model is free, _apiKey is only needed for Anthropic, and entity parameter is clarified with examples (e.g., brand names, topics). This extra meaning 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 it probes LLMs for knowledge about an entity and returns a visibility score (0-100). It specifies the default model and optional Anthropic probe, making the tool's function distinct from siblings like 'scan_competitor_ai_presence' or 'ask_pipeworx'.

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 mentions use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains how to enable Anthropic probing via _apiKey. While it doesn't list alternatives or when not to use, 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 already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description adds context about routing to one of 5,008 tools, filling arguments, and returning structured answers with citations, 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 front-loaded with key guidance and every sentence adds value. It could be slightly trimmed, but overall 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?

Given the tool's complexity (many sources, many alternatives), the description is complete: covers purpose, usage, behavior, and alternatives. No output schema needed per rules.

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 6 parameters described. The description notes aliases for the question parameter, but adds no further meaning beyond what schema provides. 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 answers factual questions using structured data from many sources, with specific examples. It distinguishes from siblings like ask_pipeworx_grounded and deep_research by noting 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 states 'PREFER OVER WEB SEARCH' and provides detailed guidance on when to use this tool vs. alternatives (ask_pipeworx_grounded, deep_research). Also gives example queries.

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. The description adds context about being a universal router with 5,008 tools, same response shape, and source-preference ranking. It discloses experimental status and live routing improvements, 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 a single paragraph of 3 sentences, conveying essential information without wasted words. It could be slightly more structured (e.g., bullet points) but remains clear 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?

For a beta tool with high complexity (5,008 tools), the description covers purpose, usage, and behavior adequately. It mentions the response shape is identical to ask_pipeworx, compensating for no output schema. However, it lacks details on the source-preference ranking mechanism, which might be useful.

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 6 parameters documented as aliases for question. The description adds no additional parameter semantics, confirming it accepts the same arguments as ask_pipeworx. Baseline is appropriate as schema handles the 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?

The description explicitly states it's a beta version of ask_pipeworx, identical in tools and arguments but with candidate routing improvements. It clearly distinguishes from the stable router (ask_pipeworx) and other siblings by specifying the experimental nature and source-preference ranking feature.

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

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

It advises to use it exactly like ask_pipeworx when wanting the newest routing, and mentions results are compared against the stable router. It also clarifies there is no fallback, making it a full working router. However, it does not explicitly contrast with other siblings like ask_pipeworx_grounded or suggest when to avoid 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?

Beyond annotations (readOnlyHint, openWorldHint, idempotentHint), the description details the internal behavior: picks the right tool from 5008 across 1318 sources, fills arguments, fetches data, extracts answer only from result, and returns specific refusal reasons. 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 well-structured, front-loading purpose and behavior, then usage guidance. Every sentence earns its place, with no fluff. About 8 sentences of dense, useful 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 defines the output structure (answer, evidence, confidence, source, fetched_at, refusal_reason) and all possible refusal reasons. The parameter shape is simple, and the tool's complexity 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 coverage is 100% with descriptions for each parameter. The description does not add significant extra meaning beyond what the schema already provides, but the schema is sufficient. 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 is a 'Hallucination-resistant answer mode for high-stakes reads' and specifies it extracts answers only from tool results. It distinguishes from the sibling tool ask_pipeworx by noting the extra cost and use case for quoted/cited answers.

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 ('high-stakes reads', 'when an answer will be quoted, cited, or acted on') and when not to ('prefer ask_pipeworx for casual lookups'). Also notes the extra LLM call cost, helping the agent decide.

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=true, idempotentHint=true, destructiveHint=false. The description adds extensive behavioral details: low-confidence matches short-circuit, closed markets return status, wide spreads are flagged, resolution rules parsed, and fallback mechanisms for news sources. 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 verbose but well-structured with bolded section headers (e.g., 'CLASSIFIERS:', 'FAN-OUT EXAMPLES:'). It is front-loaded with a clear summary sentence. While every sentence serves a purpose, the length could be overwhelming, but given the tool's complexity, it is appropriate.

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

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

The description covers all necessary aspects: input formats, processing logic, output fields, safety mechanisms, resolution risk, and edge cases. Even without an output schema, the description provides sufficient detail for an 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% with descriptions. The description adds extra context: depth enum values explained ('quick = 2-3 evidence sources, thorough = full fan-out'), default for include_raw, and examples of market_input. It also describes response shape, which is not in schema. This justifies 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: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies inputs (slug, URL, question text) and output (evidence packet + comparison). This distinguishes it from sibling tools like polymarket_edges or validate_claim, which have narrower scopes.

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 provides usage contexts: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' While it doesn't explicitly state when not to use or name alternatives, the examples and classifiers give clear guidance on 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 declare readOnly, openWorld, idempotent. Description adds rich behavioral context: data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handling of off-calendar fiscal years, sorting by primary metric, and return of paired data with citation URIs. No contradiction with annotations.

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

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

Single paragraph with multiple sentences, front-loaded with trigger phrases. Every sentence adds unique information, but could be slightly more concise. 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 complexity (two entity types, multiple data sources, sorting, return format), the description is thorough. Covers input constraints (2-5 items), data freshness (latest), sorting behavior, and output including citation URIs. No gaps for agent understanding.

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

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

Schema coverage is 100%, but description adds significant value beyond schema: explains what data each type pulls (company: revenue, net income, cash, debt; drug: adverse events, approvals, trials), how to format values (tickers/CIKs vs names), and behavioral details like sorting. Fully compensates for lack of nested objects.

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 does side-by-side comparison of 2-5 companies or drugs in one call. Uses specific verbs ('compare', 'rank') and resources ('SEC EDGAR/XBRL', 'FAERS'). Distinguishes from sibling tools like entity_profile by explicitly preferring this over sequential 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?

Provides explicit when-to-use: comparison queries, with trigger phrases. Advises to prefer over sequential single-pack lookups. Does not explicitly list alternatives or when not to use, but sibling context and practical guidance are present.

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 (read-only, open-world, idempotent), the description details internal decomposition, parallel routing, response structure (findings with evidence, confidence, gaps, contradictions), semantic excerpting, and hop fields. 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 lengthy but well-structured: front-loaded with account requirement and alternatives, then purpose, then detailed behavior. Every sentence adds value, though minor redundancy could be tightened.

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 fully explains the response format (findings packet with fields, gaps, contradictions) and covers edge cases (not signed in, breaking news, large records). No missing critical information.

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, but description adds critical context: depth values explained with facet counts, gap recovery, contradiction scan, and paid plan requirement; question parameter clarified as suitable for broad/multi-part queries.

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 performs 'grounded multi-source research across Pipeworx's 1318 STRUCTURED data sources' and contrasts with sibling 'ask_pipeworx' for single lookup and breaking news, making the tool's purpose and scope very clear.

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

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

Provides direct guidance: if not signed in, use 'ask_pipeworx' instead; prefers 'ask_pipeworx' for single lookups and breaking/current news; and explains depth-tier eligibility (thorough requires paid plan).

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

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

Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds valuable behavioral context: it returns 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed.' 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 moderately long but well-structured, with a clear purpose statement and examples. Every sentence adds value, though it could be slightly more terse. It is front-loaded with the most important information.

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

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

Given the large number of sibling tools (31), the description effectively positions discover_tools as the entry point. It explains the return format comprehensively, compensating for the lack of an output schema. The description is fully adequate for an agent to understand and invoke the tool correctly.

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

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

Schema description coverage is 100%, so baseline is 3. The description does not add significant new meaning beyond the schema; it mentions that query accepts natural language and lists aliases, but the schema already details all parameters and aliases. No enhancement.

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

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

The description starts with a clear verb+resource: 'Find tools by describing the data or task.' It lists many specific use cases (SEC filings, financials, etc.) and explicitly distinguishes from sibling tools by stating it's for discovery, not direct data access.

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 includes explicit guidance: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This establishes when to use it. However, it does not specify when not to use it or provide alternatives among the many sibling tools.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds that the tool returns multi-carrier rates with specific fields and includes an example, but does not disclose additional behavioral traits such as rate limits or authentication requirements beyond the API key parameter. The description does not contradict annotations.

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

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

The description is a single sentence plus an example, with no wasted words. It is front-loaded with the main action and structure is clear 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?

The tool has 11 parameters (4 required) and no output schema. The description explains the return value (multi-carrier rates with price, service level, delivery days) and provides an example. However, it does not elaborate on the use of optional parameters (dimensions, city/state/country), which are only documented in the schema.

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

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

Schema description coverage is 100%, so all parameters have descriptions in the input schema. The description reuses some parameter names (from_zip, to_zip, weight_oz) in the example but does not add significant new meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool gets shipping rate quotes from origin to destination for a parcel, returning multi-carrier rates with price, service level, and delivery days. It includes an example call, distinguishing it from sibling tools like easypost_track which handles tracking.

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

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

The description implicitly indicates usage for obtaining rate quotes, but there is no explicit guidance on when to use this tool versus alternatives (e.g., easypost_track for tracking). It does not provide when-not or exclusion 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds useful specifics about returned data (status, carrier, estimated delivery, scan events) 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?

Two sentences: first states purpose and returns, second gives an example. No wasted words, front-loaded with key information.

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

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

For a simple tracking tool with 3 parameters and no output schema, description adequately explains return values (status, carrier, estimated delivery, scan events). 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% (all parameters described). Description provides an example and reinforces that carrier is optional but recommended, adding limited 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?

Clear verb+resource ('Track a package by tracking number') with specific outputs listed (current status, carrier, estimated delivery date, recent scan events). Distinguishes from sibling easypost_rates which focuses on rates.

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 example call with key parameters. Clear that tracking_code is required and carrier is optional but recommended. Lacks explicit when-not-to-use or alternatives, but sibling context makes purpose obvious.

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 readOnly, idempotent, and non-destructive. The description adds significant behavioral context: fans out across multiple sources, soft-fails for patents after May 2025, fallback from GDELT to GNews. 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 information-dense, front-loaded with examples and purpose. Could be slightly more concise but structure is clear with inline lists for returned data.

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, but description fully enumerates return fields (CIK, company_name, recent_filings with URIs, fundamentals sorted, patents, news, LEI). Adequately covers complex multi-source 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% but description adds examples ('AAPL', '0000320193'), explains zero-padded CIK, and clarifies that names are not supported. This adds value beyond schema.

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

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

The description states it creates a 'full cross-source profile of a US public company' and lists specific data returns (CIK, filings, fundamentals, patents, news, LEI). It distinguishes from siblings like resolve_entity by noting name handling.

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 'ALWAYS PREFER over chaining single-pack ... lookups when the user asks for a holistic view.' Also advises using resolve_entity first if only a name is available, providing clear when-to-use and 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?

Annotations already include destructiveHint=true and idempotentHint=true. The description simply confirms deletion without adding new behavioral details 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?

Two sentences front-loaded with action and usage. No unnecessary words, efficient and clear.

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

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

For a simple 1-parameter tool with no output schema, the description is sufficient. It covers purpose, usage, and related tools, though it omits return value details which are not critical.

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

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

Schema covers 100% of parameters with description 'Memory key to delete'. The tool description adds no additional meaning or constraints 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 uses a specific verb 'Delete' and resource 'previously stored memory by key', clearly stating the action. It also distinguishes from siblings by mentioning 'remember' and 'recall'.

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

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

Provides explicit scenarios for use: stale context, task completion, clearing sensitive data. Suggests pairing with remember/recall for complementary operations, though lacks explicit 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?

Annotations already indicate readOnlyHint, idempotentHint, etc. The description adds context about the process (fetches page, extracts title/links) which is consistent with annotations and provides useful behavioral detail beyond the structured fields.

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

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

Every sentence adds value, front-loaded with the main action and output format. No redundant or vague phrasing.

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

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

Given the absence of output schema, the description adequately explains the return format (single text blob in markdown) and the tool's capabilities. It covers purpose, output, and use cases 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% with clear descriptions for both parameters. The description does not add new meaning beyond the schema, so baseline score of 3 is appropriate.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, specifying the verb 'generate' and the resource 'llms.txt file'. It differentiates from siblings by focusing on llms.txt generation, which is a distinct action from sibling tools like scan_competitor_ai_presence.

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

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

The description provides explicit use cases (getting client site indexed, drafting own llms.txt, auditing competitor visibility) but does not mention when not to use this tool or name alternative sibling tools for similar tasks.

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 readOnly, openWorld, idempotent, non-destructive. Description adds context about return fields and default active filtering. 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, front-loaded with purpose, 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 list tool with one optional parameter and no output schema, description sufficiently covers return fields 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 100% with description for include_inactive. Description adds 'active' default context but does not significantly extend 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 'List the caller's active subscriptions' with specific verb and resource. Lists return fields. Distinguishes from sibling tools like subscribe and unsubscribe.

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: use to review monitoring before adding more or to get an id to cancel. Implication of when not to use (e.g., if not managing subscriptions) but no exclusion statements.

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 limit (5/identifier/day), free usage, no quota impact, and that team reads daily. Annotations provide no additional context, so description carries full burden 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?

Three sentences: clear purpose, usage, and additional details. No fluff, front-loaded with key action.

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

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

For a low-complexity feedback tool with full schema coverage, description covers all needed aspects: purpose, when, behavior, and examples. No output schema needed.

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 context to enum values, context object optionality, and message length limit, slightly exceeding 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?

Description clearly states the tool sends feedback (bug, feature, etc.) and distinguishes from all sibling tools, none of which handle 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?

Explicitly tells when to use for bugs, features, data gaps, or praise, and what to avoid (don't paste end-user prompt).

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds further transparency: data source (CF analytics-engine), no PII, aggregation details, and caching policy (5min-1h). No contradictions.

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

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

The description is concise (5 sentences), front-loaded with the core functionality, followed by enumerated use cases and technical specifics. No redundant or unclear text.

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 one optional parameter and strong annotations, the description fully explains the return content (top tools, packs, volume), data source, privacy, and caching. No output schema needed.

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

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

The schema covers the single parameter 'window' with enum values and description. The tool description reinforces the meaning of windows (short vs long) and ties them to use cases, adding value beyond the schema.

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

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

The description clearly states the tool returns 'top tools, top packs, and total call volume' over a recent window. It distinguishes from siblings like ask_pipeworx and search tools by focusing on aggregated trending data, not direct 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 lists three use cases: discovering hot data sources, confirming canonical tools, and aligning use case with agent needs. It doesn't specify when not to use, but the context is clear.

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

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

Adds significant behavioral context beyond annotations: describes monotonicity checks, partition sum checks, Jaccard similarity filter, placeholder filtering, and fill check with realizable edge. No contradiction with annotations (readOnlyHint, idempotentHint, etc.).

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?

Long but well-structured, starting with purpose then detailing modes and logic. Each sentence adds value given the complexity, though could be slightly more concise.

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

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

Covers return values (opportunities[], partition_check, fill_check) despite no output schema. Lacks error cases and behavior when no opportunities found, but still fairly 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 description coverage is 100%, but the description enriches meaning: for 'event' it explains child market walking and partition_check; for 'topic' it explains cross-event search and comparator. Provides example values and use-case context 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 finds arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes three modes (no args, event, topic) with specific use cases, making it distinct from sibling tools like polymarket_edges or polymarket_fill_risk.

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: 'Call with NO args for a trending_scan', 'event (recommended for a specific market)', 'topic (for cross-event scanning)'. Includes examples and recommendations, as well as when to use polymarket_fill_risk for custom sizing.

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, no destructive action. The description adds substantial behavioral detail: caching at KV level keyed on knobs, three model families with specific calculations, edge components (edge_pp_net, kelly_fraction), 24h-move warning, Fed bet exclusion, and diagnostics. 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 long but well-structured, with clear sections for overview, segments, opportunity fields, knobs, and response format. Every sentence adds value, though it could be slightly trimmed 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?

For a complex tool with 9 parameters, 3 segments, and no output schema, the description covers all essential aspects: purpose, edge calculation, response structure (by_segment, fed_candidates, diagnostics), tradeable-edge knobs, and caching policy. 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 coverage is 100%, and the description adds meaningful context beyond schema descriptions, e.g., explaining min_partition_leg_kelly's role vs min_kelly, and why slippage_pp defaults to 0.3 (zero fees but bid/ask depth). This adds value for correct usage.

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

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

The description clearly states the tool's action: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It also frames the use case ('what should I bet on today'), and the verb 'discover' distinguishes it from sibling tools like polymarket_arbitrage or polymarket_edge_tracker.

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 the tool is for discovering betting opportunities without paging markets, and provides context on caching (1h) and tradeable-edge knobs. It doesn't explicitly state when not to use it, but the purpose is clear enough given sibling tool names.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds significant behavioral details: it returns tracked opportunities with full time-series, first_seen, trend, and decay rates; expired opportunities with lifespan; snapshot dates; and limitations like 60-day TTL and snapshot start dependency. 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 well-structured with a clear purpose, argument list, and response breakdown. It is relatively concise for the amount of information conveyed, though slightly dense. The key question is front-loaded, and every sentence adds value.

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

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

Without an output schema, the description thoroughly explains the return structure (tracked, expired, snapshot_dates) and includes important details like sign interpretation and decay computation. It addresses limitations and data availability, making it fairly complete for its complexity.

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

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

Schema description coverage is 100% with clear parameter descriptions. The description adds context about default values and the meaning of window families, but does not significantly enhance the parameter semantics beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: providing edge persistence and decay telemetry from daily snapshots, answering how long an edge has existed and if it's shrinking. It distinguishes from similar tools like polymarket_edges by focusing on historical trends rather than current 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?

The description gives a clear use case (comparing a fresh wide edge vs. a 3-week-old wide edge) and explains what questions it answers. While it doesn't explicitly list alternatives or when not to use it, the context is sufficient for an AI agent to infer appropriate usage.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds rich behavioral context: it walks the ladder, returns specific fields (slippage, shares filled, verdict), warns about thin legs and forced directional risk. No contradictions; the description enhances transparency 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 comprehensive but slightly verbose; however, it uses clear structural elements (REQUIRES, SINGLE-MARKET, BASKET) and every sentence provides value. A bit long for quick scanning, but the clarity gained justifies the length. Not perfect, but very good.

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 lists return fields, explains the two modes with examples, and details edge cases (thin_legs, forced_directional_risk). It covers all necessary context for an AI agent to understand usage, input parameters, and expected output. Complete for this 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 the description adds significant meaning: explains side defaults and auto-detection for baskets, clarifies size_usd interpretation in each mode (max spend vs target proceeds vs settlement notional), and provides ranges, units, and logical behavior. This is far beyond the baseline of 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 performs a realizable-vs-theoretical edge check against live order-book depth, with explicit single-market and basket modes. It distinguishes itself from siblings like polymarket_arbitrage and polymarket_edges by focusing on fill risk verification, using specific verbs like 'checks', 'walks the ladder', and 'returns verdict'.

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 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It provides clear when-to-use guidance, explains why (theoretical overround not capturable, partial baskets cause unhedged positions), and distinguishes single-market vs basket usage with REQUIRES syntax.

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 safety fields (compatibility_warning, temporal_alignment, skipped_cross_type) that go well beyond annotations (readOnlyHint, idempotentHint). Clearly describes behavior when spreads are valid vs not.

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?

Front-loaded with purpose, then modes, response, safety. Every sentence adds value, though somewhat verbose. Could be slightly trimmed 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?

Despite no output schema, description fully explains response format (leg-by-leg prices, matched spreads, safety fields). Covers all aspects needed for an agent to use 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%, and description adds meaning by explaining the two modes, providing examples, listing valid topic values, and noting that explicit parameters override topic-mapped values.

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

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

Clear verb+resource: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' Distinguishes from siblings like polymarket_arbitrage and polymarket_edges by explicitly focusing on cross-venue comparison.

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?

Explains two modes (topic vs explicit) and when compatibility_warning indicates spreads are not meaningful. Lacks explicit 'when not to use' compared to specific alternatives, but 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 declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds scoping to identifier (anonymous IP, BYO key hash, account ID) and pairing with remember/forget. 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?

Four focused sentences, front-loaded with main action. Every sentence adds value: retrieval, listing, scoping, and pairing. 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 tool with one optional parameter and no output schema, the description covers retrieval, listing, scoping, and relationships. It doesn't describe return format but is sufficient given simplicity.

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%. The description explains the key parameter's dual role (retrieve vs list) with examples like 'omit to list all keys' and 'user_research_topic', 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 uses specific verbs 'retrieve' and 'list' with the resource 'value saved via remember' or 'all saved keys'. It clearly distinguishes from sibling tools '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?

The description explains when to use the tool: to look up previously stored context without re-deriving. It mentions omitting the key to list all keys. No explicit exclusion or alternatives, 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?

Annotations already indicate readOnlyHint, idempotentHint, etc., but the description adds significant behavioral context: what each returned alert contains, the effect of mark_read on subsequent calls, and that polling works fine. 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 reasonably concise for the amount of information it conveys. It front-loads the main purpose and then adds details. Could be slightly tighter, but overall efficient.

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

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

Given 5 optional parameters and no output schema, the description comprehensively covers input semantics and output shape (source, citation_uri, raw payload). It also addresses usage patterns (polls, alternative feed) making it self-contained.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds value by explaining the purpose of type, since, and mark_read parameters in the context of the overall functionality. It doesn't repeat the schema but provides meaningful integration.

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 'Pull fired events from your subscription feed' and specifies the returned fields (source, citation_uri, raw payload). It provides filtering options. However, it does not explicitly distinguish itself from sibling tools like list_subscriptions, leaving some ambiguity.

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 when to use this tool (e.g., polling) and provides context for filtering (type, since) and mark_read behavior. It also mentions an alternative endpoint for scripts/dashboards. Missing 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?

Annotations already declare safe read-only, idempotent behavior. The description adds critical details: fallback mechanics, USPTO soft-fail, return structure (changes[], total_changes, citation URIs), and window handling. 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?

Single paragraph is dense but well-structured with examples first, then behavior, then fallback. Could be more scannable with bullet points, but no superfluous sentences.

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

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

Given no output schema, the description adequately describes return structure, source grouping, and citation URIs. It covers edge cases (rate limits, API sunset) and provides complete guidance for a multi-source tool with 100% schema coverage.

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 good descriptions, but the description adds value: explains since parameter with ISO date and relative shorthand examples, notes type is limited to company, and provides usage hints like 'Use "30d" or "1m" 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 clearly states the tool aggregates changes from SEC EDGAR, GDELT/GNews, and USPTO for a company. It provides example queries and distinguishes from sibling tool entity_profile, fulfilling the specific verb+resource+scope criterion.

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 contrasts with entity_profile for static profiles and explains fallback logic (GDELT preferred, GNews on rate limits). It lacks explicit when-not-to-use scenarios but provides sufficient context for proper 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 indicate idempotentHint=true and destructiveHint=false, which align with a safe store operation. The description adds context about scoping by identifier, persistence duration, and pairing with recall/forget. However, it doesn't explicitly state that saving with the same key overwrites the value, which would be useful.

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 three sentences, front-loading the purpose. Every sentence adds value, but the third sentence could be slightly more concise. Still, 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 no output schema, the description doesn't specify return value format, but it's acceptable for a simple store tool. It covers persistence, use cases, and pairing, which is 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 coverage is 100%, so the description's parameter info adds minimal value beyond the schema. It provides example key prefixes but no additional constraints or formatting 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?

The description uses a specific verb ('Save') and resource ('data'), and distinguishes from sibling tools like recall and forget by explaining the pairing. It gives concrete examples of what to store, making the purpose crystal clear.

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

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

Explicitly states when to use ('when you discover something worth carrying forward'), and what not to use it for (pair with recall/forget). It also differentiates persistence behavior for authenticated vs anonymous sessions.

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

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

The description goes beyond annotations by detailing internal cascading lookups and specifying return values for each type (company: ticker + CIK + name + citation URI; drug: RxCUI + ingredient + brand + citation). Annotations already indicate read-only, idempotent behavior, and description adds rich behavioral context without contradiction.

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

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

The description is front-loaded with the core purpose and usage, followed by detailed type-specific returns. It is slightly verbose but each sentence adds value; no 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?

The description covers return values, citation URIs, and the cascading lookup behavior. Given no output schema, it provides sufficient detail for an agent to understand what the tool returns and how to use the 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 coverage is 100%, and the description adds substantial meaning: for 'value', it distinguishes input formats for company vs drug and provides examples. For 'type', it explains what each enum value returns. This adds value beyond the schema.

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

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

The description clearly states the tool resolves names to canonical identifiers, with examples like ticker, CIK, and RxCUI. It specifies supported types and distinguishes from sibling tools by emphasizing it is the first step when needing an ID.

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 'Use FIRST whenever you have a name but need an ID.' It provides concrete query examples but does not explicitly state when not to use or mention alternative tools for comparison.

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 non-destructive. The description adds behavioral details: it probes each entity with ai_visibility_check, ranks by score, and returns specific fields like score, confidence, signal density. 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 concise sentences plus an example. It is front-loaded with the core action. Each sentence adds value without redundancy or fluff.

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

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

Given the tool's complexity (4 parameters, no output schema, rich annotations), the description fully explains what it does, how it works (probes with ai_visibility_check), and what output to expect (ranked list with specific fields). It is self-contained and 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 100%. The description adds extra semantics: 'entities' first entry is treated as subject; 'models' lists supported values and default; '_apiKey' explains condition; 'context' adds usage hint. All parameters are clearly explained 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 verb (compare), resource (AI visibility of entities), and purpose (competitive AI-marketing audits). It distinguishes from sibling 'ai_visibility_check' by describing it as a batch comparison that probes each entity and ranks results.

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 a clear use case ('competitive AI-marketing audits') and an example question. It implicitly suggests when to use but does not explicitly state when not to use or list alternatives beyond mentioning the underlying probe.

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, open-world. Description adds behavioral details: composite check across two services, bundlephobia's first measurement can take 5-30s, partial failures degrade gracefully with sources_failed reporting. 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 well-structured and front-loaded with purpose, but slightly verbose. Every sentence adds value, though could be slightly more concise. Still appropriately sized 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?

Even without an output schema, the description thoroughly explains the return fields (summary block, per-advisory detail, links, alternative versions) and covers ecosystem limitations. Complete for a scanning tool with partial failure behavior.

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

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

Schema coverage is 100%, so the schema already documents both parameters. Description adds extra clarity: package accepts scoped packages, version defaults to latest. This adds marginal 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 it is a composite check for npm packages covering multiple sources (deps.dev and bundlephobia), distinguishing it from sibling tools like scan_competitor_ai_presence. It specifies the verb 'scan' and resource 'dependency', and explicitly notes the NPM ecosystem scope.

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

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

Provides explicit guidance on when to use: 'whenever an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me''. Also notes exclusions (PyPI, Maven, etc.) and alternative tools (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 already declare readOnlyHint and idempotentHint. Description adds specifics: embedding model, window size, character cap, truncation, and output format.

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

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

Every sentence adds value; front-loaded purpose and usage, then technical details. 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, but description explains return values (passages with offsets and scores). Covers all aspects needed 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%, baseline 3. Description adds examples for query, notes max chars for text, and explains limit default, 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 verb (semantic search) and resource (inside a fetched record), and distinguishes from siblings by emphasizing context-saving and pairing with ask_pipeworx_grounded.

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 when the record is too big to cram into the prompt' and pairs with ask_pipeworx_grounded. Does not explicitly say when not to use, 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 indicate creation (readOnlyHint=false) and non-destructiveness (destructiveHint=false). The description adds behavioral details: returns subscription id, delivery channels (always-on feed, optional email/sms with rate limits and verification), and type-specific configurations. 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 somewhat lengthy but well-organized: purpose first, then prerequisites, types, and delivery options. Every sentence adds essential information, with no redundancy. Could be slightly more concise, but structure is logical.

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 (3 parameters, nested objects, multiple subscription types, no output schema), the description is highly complete. It covers prerequisites, return value, type-specific parameters, delivery channels, rate limits, and verification steps, leaving minimal ambiguity.

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 the description adds substantial value by elaborating on each parameter: type enum descriptions, detailed parameter formats for each type, and delivery object with constraints (phone verification, rate limits, webhook signing). This goes well beyond the schema alone.

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

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

The description clearly states the tool's purpose: 'Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id.' It distinguishes from siblings like 'unsubscribe' and 'list_subscriptions' by focusing on creation.

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, including prerequisite ('Requires a Pipeworx OAuth account'), supported alert types with examples, and delivery options. It does not explicitly state when not to use or name alternatives, but the guidance is sufficient for proper 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 declare readOnlyHint, openWorldHint, etc., but the description adds behavioral context beyond those: it explains that the tool returns example questions with exact tool+argument shape from a live catalog, and it describes the behavior with and without the topic argument.

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 lengthy but each sentence adds meaningful information. It is front-loaded with common user queries and structured to convey purpose, usage, and behavior efficiently. A minor reduction in redundant phrasing could improve conciseness.

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

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

Given the tool's simplicity (one optional parameter, no output schema) and the richness of the provided annotations, the description is fully adequate. It explains what the tool returns, how to invoke it, and its role in the agent's workflow, 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 coverage is 100% and the schema already describes the topic parameter well. The description adds further value by explaining the effect of omitting the topic (full spread) versus providing it (focused), and by listing example values not enumerated 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 tool's purpose is clearly articulated: it is an onboarding entry point that returns category-bucketed example questions. The description distinguishes it from sibling tools by stating to use it first when unsure of Pipeworx's capabilities, and it names specific meta-tools as alternatives.

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 guidance is provided: 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools…' It also explains the effect of omitting or providing the topic parameter, offering clear context for when to use this tool versus alternatives.

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

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

The description adds significant value beyond annotations: it discloses that the row is deactivated (not deleted) and that historical events remain available via 'recent_alerts'. This aligns with the destructiveHint=false annotation and idempotency.

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 two sentences. The first sentence front-loads the primary action, and the second adds necessary context 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?

For a simple mutation tool with one parameter and no output schema, the description covers ownership, side effects, and data persistence completely. The tool's behavior is fully understandable.

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 only parameter 'id' is fully described in the schema (subscription uuid). The description does not add additional semantic detail beyond the schema, but schema coverage is 100%, so baseline 3 is appropriate.

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

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

The description clearly states the verb 'Cancel' and the resource 'subscription by id', directly indicating what the tool does. It distinguishes itself from sibling tools like 'subscribe' (which creates subscriptions) 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?

The description explains ownership enforcement ('you can only cancel your own subscriptions'), providing a clear condition for use. It does not explicitly list alternatives, but the context from sibling tools 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 indicate readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds significant context: exact percent-delta math for financial claims, automatic fallthrough to grounded pipeline for others, and details on return types (verdict, actual value with citation, reasoning). 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 front-loaded with trigger phrases and a clear two-line overview. It is structured to cover usage, behavior, and return values. While somewhat lengthy, every sentence adds value and it avoids redundancy. Mildly verbose but well-organized.

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 moderate complexity (two processing paths, two parameters, no output schema), the description fully explains the behavior, verdict types, return format, and the distinction between financial and other claims. It leaves no obvious gaps for an agent to interpret.

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 adds useful semantic context: tolerance_pct overrides default, default is implied by wording and capped at 5, and suggests using 1-2% for hallucination detection. This goes beyond the schema's property 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 identifies the tool's purpose with trigger phrases ('Is it true that…', 'fact check') and distinguishes it from siblings by detailing its specialized handling for company-financial claims vs. other claims. It states the output verdict types, making it easy for an AI to recognize when to invoke this 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 states 'Use whenever the agent needs to check whether something a user said is factually correct.' It explains the two distinct processing paths (structured EDGAR for financial, grounded pipeline for others) and notes it replaces 4-6 sequential calls, implying efficiency. However, it does not explicitly mention scenarios where this tool should NOT be used.

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