Comicvine

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

Annotations already indicate readOnly, non-destructive, open-world behavior. The description adds valuable context: the tool routes to 2,520 tools, fills arguments, and returns structured answers with stable citation URIs. This 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 somewhat long but well-structured with clear guidance upfront. Every sentence contributes value, though it could be slightly more concise without losing essential context.

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 (handling many data sources), the description is comprehensive: it covers the types of data, the routing mechanism, citation format, and examples. No output schema is needed as the description explains the return style.

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

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

Schema coverage is 100% for the single 'question' parameter, and its description is adequate. The description does not add further parameter-level detail beyond the schema, 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 tool's purpose: answering factual questions using authoritative structured data, with a preference over web search. It specifies a wide range of data types and provides examples, distinguishing it from sibling tools.

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

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

The description explicitly advises to prefer this tool over web search for factual queries, provides indicative query patterns ('what is', 'look up', etc.), and lists concrete examples, making it clear when to use this tool.

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

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

Annotations (readOnlyHint, openWorldHint, destructiveHint=false) indicate a safe read operation. The description adds behavioral context: it fans out to relevant packs, classifies bets, and returns a comparison. No contradictions.

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

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

The description is a single paragraph covering purpose, inputs, behavior, and use cases. Every sentence adds value with no filler. It is front-loaded with the core action.

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

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

Despite no output schema, the description fully explains the output (evidence packet, market-vs-model comparison) and internal logic (fan-out to packs). It is complete for a tool of this 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?

Input schema has 100% description coverage. The description adds value by explaining the 'market' parameter accepts slugs, URLs, or question text, and 'depth' toggles between quick and thorough. This elaborates on the schema definitions.

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

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

The description specifies the tool's function: research a Polymarket bet by pulling Pipeworx data. It clearly states inputs (slug, URL, question text) and outputs (evidence packet, market-vs-model comparison). It distinguishes from siblings like polymarket_arbitrage and polymarket_edges by being the dedicated research tool.

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

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

The description provides explicit use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It does not explicitly state when not to use or name alternatives, but the context is clear.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the tool is clearly a safe read operation. The description adds no additional behavioral context, such as return format, pagination, or other side effects. It merely restates the read-only nature.

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

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

The description is extremely concise (two words), but it is under-specified and does not earn its place. It lacks the substance needed to be helpful.

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

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

Given the low complexity (1 parameter, no output schema), the description is completely inadequate. It does not explain the return value, the source of characters, or any other context needed for effective use.

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

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

The input schema has one parameter (id) with 0% description coverage, and the tool description does not explain what the id represents or any constraints. The description fails to compensate for the lack of schema documentation.

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

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

The description 'Character detail.' minimally states that the tool retrieves details about a character, but it does not specify what character or source, nor does it distinguish from the sibling tool 'characters' (likely a list). It is not a tautology but lacks specificity.

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

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

No guidance is provided on when to use this tool versus alternatives. There is no mention of context, prerequisites, or when not to use it. The description is silent on usage 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 indicate readOnlyHint=true and destructiveHint=false, but the description adds no additional behavioral context. For example, it doesn't mention that results are sorted by default or pagination limits.

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

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

Overly brief to the point of ambiguity. 'Character list.' is not a complete sentence and fails to convey the tool's purpose clearly.

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 four parameters, no output schema, and low schema coverage (25%), the description is critically incomplete. It does not explain return format, default behavior, or how to use the tool effectively.

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

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

Only one of four parameters (filter) has a description in the schema. The description 'Character list.' provides no explanation of parameters like sort, limit, or offset, leaving the agent to infer their roles.

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

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

The description 'Character list.' is a tautology of the tool name, failing to specify the source or scope of characters. It lacks a verb like 'lists' or 'retrieves' and does not distinguish from the sibling tool 'character'.

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

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

No guidance on when to use this tool versus alternatives like 'character' or 'people'. No context on filtering or sorting behavior is provided.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds significant detail: data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), specific fields (revenue, net income, etc.), and output format (paired data with citation URIs). 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 concise and front-loaded with the core purpose. It efficiently covers usage triggers, type-specific details, and benefits in a few sentences with no redundant information.

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

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

For a simple tool with two parameters and good annotations, the description covers purpose, usage, data sources, and return format. It could mention error handling or response structure in more detail, but it is sufficient for correct invocation.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds meaningful context: for 'type' it explains which data is pulled per entity type, and for 'values' it provides examples (tickers, drug names). This goes 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 compares 2-5 companies or drugs side by side in one call, providing a specific verb and resource. It distinguishes itself from sibling tools by offering a unique comparison capability, and includes explicit example 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 lists specific user expressions that trigger use ('compare X and Y', 'X vs Y', etc.) and notes it replaces 8-15 sequential calls. It lacks explicit when-not-to-use or alternative tools, but the context is clear enough for an agent to 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 already convey read-only and non-destructive nature. Description adds that it returns top-N relevant tools, but no further behavioral details are needed. Description is consistent with annotations.

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

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

Description is front-loaded with purpose and usage guidelines. It is reasonably concise, though slightly verbose with the list of domains. Still efficient for its length.

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

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

Given the large set of sibling tools, this description provides necessary context for a discovery tool. It explains what it returns and gives examples to help formulate queries, making it complete for its role.

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

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

Input schema has 100% description coverage for both parameters. Description adds no additional meaning beyond the schema (e.g., limit default and max are already in schema). 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's purpose: 'Find tools by describing the data or task.' It provides a specific verb-resource pair and distinguishes itself from sibling tools by positioning itself as a discovery tool to be used first.

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 advises 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' Also lists example domains, giving clear guidance on appropriate use cases.

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

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

Annotations already indicate readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds significant behavioral detail: it returns data from SEC, USPTO, news, and GLEIF, provides pipeworx:// citation URIs, and confirms it is a read-only aggregation. 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: first sentence states purpose and use cases, second sentence details output and parameter constraints. Every word adds value, no fluff, and critical information is front-loaded.

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

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

Despite having no output schema, the description thoroughly covers the return types (SEC filings, financials, patents, news, LEI), the parameter constraints, and the usage context. For a complex multi-source tool, this is fully sufficient.

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% as both parameters have descriptions. The description adds value beyond the schema by explaining that type only supports 'company' (with 'person/place coming soon') and that value accepts ticker or zero-padded CIK, explicitly stating names are not supported.

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 ('Get everything about a company') and resource, clearly stating the tool's purpose. It lists exact use case queries ('tell me about X', 'give me a profile of Acme') and distinguishes from sibling tools like resolve_entity by explaining that names are not supported.

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 the tool (when user asks for a company profile or when multiple pack tools would be needed) and when not to use it (if only a name is available, use resolve_entity first). This provides clear guidance on usage context.

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

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

Annotations already mark it destructive, but the description adds context on why (clear sensitive data) 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?

Three sentences: action, usage guidance, related tools. Each sentence earns its place; 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 1-param destructive tool without output schema, the description covers purpose, usage, and relationships completely.

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

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

Schema documentation covers the single parameter fully (100% coverage); description adds no extra semantic meaning beyond 'by key'.

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

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

The description clearly states it deletes a memory by key, distinguishing it from siblings like 'remember' and 'recall' by mentioning pairing.

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 scenarios (stale context, task done, clear sensitive data) and hints at alternatives by naming related 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 clearly state readOnlyHint=true, destructiveHint=false, so the behavioral profile is covered. Description adds no extra insight beyond annotations, but does not contradict them.

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

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

Two words is overly terse; the description is under-specified and fails to provide meaningful 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 simple schema and annotations, the description is insufficient. It does not explain the return value or how 'detail' differs from other tools.

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 one required param 'id' (number) with 0% description coverage. The description does not explain what 'id' refers to (e.g., issue ID), leaving the agent to infer from the name.

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 'Issue detail.' implies retrieving details of a single issue, but lacks a verb and does not distinguish from sibling 'issues' (likely list) or other entity tools. Purpose is vaguely implied.

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

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

No guidance on when to use this tool versus alternatives like 'issues' or other get tools. No exclusions or context provided.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description 'Issue list.' adds no behavioral detail beyond the annotation, such as pagination, sorting, or filtering behavior.

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

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

The description is extremely short (two words) but lacks substance. It is under-specified rather than concise, providing no structure or helpful 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 four parameters, no output schema, and the presence of sibling tools, the description is completely inadequate. It does not explain what the tool returns, how to use parameters, or how it relates to other tools.

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

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

With 25% schema description coverage, the description does not explain any of the four parameters (sort, limit, filter, offset). The filter parameter's schema description is minimal, and the tool description provides zero additional meaning.

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

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

The description 'Issue list.' is vague and does not specify what kind of issues (e.g., from which system or context). It provides a minimal verb+resource but lacks differentiation from sibling tools like 'issue' or 'search'.

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

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

No guidance is provided on when to use this tool versus alternatives like 'issue', 'search', or 'entity_profile'. The description fails to mention any specific use cases or exclusions.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds no behavioral detail, such as default sort order, pagination, or rate limits.

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

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

Extremely short, but the brevity sacrifices essential information. It is under-specified rather than concise.

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

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

With 4 unannotated parameters, no output schema, and rich sibling context, the description fails to provide even minimal completeness for an agent to select and invoke the tool correctly.

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

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

Schema description coverage is only 25%, with only 'filter' having a description. The description 'Creators.' provides no additional meaning for the four parameters.

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 'Creators.' is vague, only hinting at the domain. It does not specify the action (e.g., list, search) or distinguish from sibling tools like 'person' or 'characters'.

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

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

No guidance on when to use this tool versus alternatives. The description lacks any context about filters, results, or use cases.

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

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

Annotations already indicate readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds no behavioral context beyond the structured data, such as what 'detail' encompasses or any limitations.

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

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

The description is extremely brief (two words) but at the cost of clarity and utility. It is underspecified, not truly 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?

Given the simple schema (1 param, no output schema), the description should explain the return value or scope of 'detail'. It fails to provide sufficient context for an AI agent to invoke correctly.

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

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

The input schema has 0% description coverage, and the description does not explain the 'id' parameter (e.g., whose ID, what format). The phrase 'Creator detail' does not add semantic meaning to the parameter.

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

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

The description 'Creator detail' is a noun phrase, not a clear action. It vaguely suggests retrieving details about a creator, but lacks a verb like 'get' or 'retrieve'. It does not distinguish from sibling tools like 'character' or 'people'.

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

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

No guidance on when to use this tool versus alternatives. The context signals show sibling tools like 'people' (plural) and 'entity_profile', but the description gives no comparison or usage context.

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

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

Annotations (readOnlyHint=false) indicate a write operation, which matches the description's purpose of sending feedback. The description adds critical behavioral details: rate-limited to 5 per identifier per day, free, and does not count against tool-call quota. No contradiction with annotations.

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

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

The description is concise (5 sentences) and front-loaded with purpose. Every sentence adds value. It could be slightly more structured (e.g., bullet points) but is 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?

Given the tool's simplicity (no output schema, 3 parameters), the description covers all necessary information: when to use, how to format, rate limits, and quota behavior. It is complete for an agent to invoke correctly.

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

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

Input schema has 100% coverage with good descriptions. The description adds value by reinforcing enum meanings and providing usage nuance like 'don't paste the end-user's prompt', which goes beyond schema capabilities. Baseline 3 + extra context = 4.

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

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

The description clearly states the tool's purpose: reporting broken items, missing features, or praise to the Pipeworx team. It explicitly lists use cases (bug, feature/data_gap, praise) and distinguishes from sibling tools like 'ask_pipeworx' and 'issue' by focusing on feedback to the development team.

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

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

The description provides explicit guidance on when to use each type (bug, feature/data_gap, praise) and includes important context: free usage, no quota impact, rate limiting, and formatting instructions ('describe issue in terms of Pipeworx tools/packs'). This helps the agent decide correctly.

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

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

Annotations already declare readOnlyHint true and destructiveHint false; description adds that the tool searches, groups markets, checks monotonicity, and returns ranked opportunities with reasoning. No contradiction. Minor deduction for not mentioning rate limits or authentication, but not necessary given read-only nature.

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

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

Single paragraph but well-structured: first sentence states purpose, followed by clear enumeration of two modes with markers. Every sentence is purposeful and informative. 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?

Given complexity (two modes, arbitrage detection) and no output schema, the description provides enough detail for an agent to understand what the tool does and how to invoke it. It describes return format (ranked opportunities with trade direction and reasoning). Slight deduction for lack of return schema, but still sufficiently 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 covers 100% with descriptions; description adds significant value by explaining how each parameter triggers a different mode, what each mode does, and provides examples. This goes well beyond basic parameter names.

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

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

Clearly states the tool finds arbitrage opportunities on Polymarket by checking monotonicity violations, with two distinct modes (event and topic) explained. Distinguishes from siblings; no other sibling seems to perform arbitrage detection.

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

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

Explicitly explains when to use each mode: event mode for a single event slug, topic mode for cross-event scenarios. Provides an example where topic mode catches cases that event mode would miss (e.g., multiple events for different cutoff dates).

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

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

Description details the algorithm (grouping by asset, computing model probability, ranking by edge) beyond the readOnly/readHint annotations. It adds context about the lognormal model and live data sources 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?

Description is a well-structured paragraph with front-loaded purpose. All sentences contribute value, though it 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?

Given no output schema, the description explains return format (top N ranked, suggested trade direction). For a tool with 3 optional params and clear annotations, it is adequately 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 coverage is 100%, so the baseline is 3. The description does not add per-parameter guidance beyond the schema, but the overall workflow explanation is sufficient.

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 scans high-volume Polymarket markets and returns those where Pipeworx data disagrees with market price, specifying the resource and action. It distinguishes from siblings like 'polymarket_arbitrage' by focusing on edge detection.

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

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

Description targets the 'what should I bet on today' question and notes it covers crypto-price bets, providing clear context. It does not explicitly exclude cases or name alternatives, but the usage scenario is well-defined.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety profile is covered. However, description adds no behavioral context (e.g., pagination behavior, data scope). Beats a 1 because annotations exist but fails to add value.

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

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

Extremely short but under-specified. Conciseness is about efficiency without sacrificing meaning; here meaning is sacrificed.

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 4 parameters, no output schema, and low schema coverage, description is wholly inadequate. Does not explain return format, filtering behavior, or 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 description coverage is only 25% (filter has a description). Description does not explain any parameter's meaning or usage, providing no added 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?

Description is just 'Publishers.' which is a noun not a verb+resource. It does not state what action the tool performs, making it a tautology.

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

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

No guidance on when to use this tool versus siblings like 'people' or 'issues'. No when-to-use or when-not-to-use information.

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

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

Annotations already declare readOnlyHint=true, and the description reinforces safe behavior. Additionally, it discloses scoping ('Scoped to your identifier') and the listing effect when key is omitted, adding valuable context beyond annotations.

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

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

Three sentences, all valuable. The first sentence clearly defines the action. The second provides usage rationale. The third clarifies scoping and pairs with siblings. Could be slightly more compact but 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?

Given simple schema (one optional param, no output schema), the description fully covers what the tool returns (value or list), its scoping, and its relationship to siblings. No output schema needed as behavior is straightforward and described.

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 one parameter. Description explains the meaning of providing vs. omitting the key, which the schema's description ('Memory key to retrieve (omit to list all keys)') already conveys, but the description adds context about what the retrieved value represents (a previously saved value) and the listing behavior.

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

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

Clearly states verb (retrieve) and resource (saved value/keys). Distinguishes from siblings (remember, forget) by explaining the retrieval action. Explicitly covers the dual behavior when key is omitted.

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 ('look up context the agent stored earlier') and implicitly when not (save/delete with siblings). Names alternatives: 'Pair with remember to save, forget to delete.' Provides concrete use cases.

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

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

Annotations indicate a safe read operation (readOnlyHint=true, destructiveHint=false). The description adds significant behavioral detail: it fans out to three sources in parallel, explains the since parameter formats (ISO date or relative shorthand), and describes the return structure (structured changes + total_changes count + citation URIs). No contradictions.

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

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

The description is detailed yet efficient, front-loading the purpose and usage, then providing technical details about sources, parameters, and output. It is appropriately sized without unnecessary fluff.

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

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

Given the tool has 3 required parameters and no output schema, the description covers purpose, sources, parameter formats, and return structure. It lacks error handling or rate limit info, but for a read-only tool with strong annotations, it is sufficiently 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 coverage is 100%, so all parameters have descriptions. The description adds value by explaining the since parameter's accepted formats (ISO date or relative shorthand like '7d', '30d', '1y'), giving examples, and clarifying the type parameter's limitation ('Only company supported today'). This goes beyond the schema's basic descriptions.

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

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

The description clearly states 'What's new with a company in the last N days/months?' and provides example queries like 'what's happening with X?', 'brief me on what happened with Microsoft this quarter'. It specifies the tool fans out to multiple sources (SEC EDGAR, GDELT, USPTO) and returns structured changes, distinguishing it from siblings like 'entity_profile' or 'search'.

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 usage guidance: 'Use when a user asks...' and lists concrete query patterns. It also mentions monitoring for changes. While it doesn't explicitly state when not to use, the examples provide clear context.

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

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

Annotations are readOnlyHint=false and destructiveHint=false, which the description complements by stating it's a write operation and adding context about session scoping and persistence duration (24 hours for anonymous, persistent for authenticated). This goes beyond the annotations.

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

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

The description is concise at four sentences, front-loaded with purpose and usage, then behavioral details. No unnecessary words.

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

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

While the description covers use cases and persistence, it does not mention the return value or confirm success. With no output schema, the agent is left guessing about the tool's response.

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 adds context about key-value storage and example keys, but this does not significantly improve semantic understanding beyond the schema.

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

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

The description clearly states the tool saves data for reuse, specifying the verb 'save' and resource 'data'. It distinguishes from siblings 'recall' and 'forget', which are mentioned explicitly.

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

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

The description provides explicit when-to-use guidance: 'when you discover something worth carrying forward' with concrete examples. It also contrasts with recall and forget for retrieval and deletion, and discusses scoping and persistence differences.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, signaling safe read operations. The description adds value by specifying that the return includes identifiers plus 'pipeworx:// citation URIs'. It does not describe error handling or limits, but for a safe lookup tool, this is sufficient transparency. 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 moderately long but each sentence serves a distinct purpose: purpose, use case, examples, workflow hint, and efficiency claim. It is front-loaded with the core action and resource. The structure is logical and easy to parse, though one or two redundant phrases could be trimmed for maximal 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?

The description covers the what (identifier lookup), why (need for official IDs), when (before other tools), and a hint of output format (IDs + citation URIs). There is no output schema, but the description compensates by naming return systems. Given the tool's simplicity and the richness of annotations, the description is sufficiently complete for an AI agent.

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

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

Schema description coverage is 100%: both 'type' and 'value' have clear descriptions. The description goes further by explaining the meaning of values for different types (e.g., for company: 'ticker (AAPL), CIK (0000320193), or name') and provides illustrative examples. This adds context beyond the schema's enum and string definitions.

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

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

The description clearly states the action ('Look up the canonical/official identifier') and specifies the resource type ('company or drug'). It lists the specific identifier systems (CIK, ticker, RxCUI, LEI), making the purpose highly distinct from sibling tools which are unrelated (e.g., 'ask_pipeworx', 'character'). The verb 'resolve' is effective and matches the name.

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

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

The description explicitly tells when to use the tool: 'when a user mentions a name and you need the CIK...' It also provides a workflow directive: 'Use this BEFORE calling other tools that need official identifiers.' It names specific alternatives it replaces ('Replaces 2–3 lookup calls') and gives concrete examples ('Apple → AAPL'). This is exceptionally 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 read-only, non-destructive behavior. The description adds no further behavioral details (e.g., pagination behavior, scope, or limitations), missing an opportunity to provide value beyond what annotations offer.

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?

Extremely brief but under-specified. The description could include more useful information in the same space without being verbose.

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

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

The tool has 4 parameters, no output schema, and many siblings. The description provides almost no context about the resources searched, parameter roles, or result format, making it insufficient for effective tool selection.

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 low (25%). The description does not explain the meaning or usage of 'page', 'limit', 'query', or 'resources'. It fails to compensate for the incomplete 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 phrase 'Multi-resource search' indicates the tool searches across multiple resources, adding minimal context beyond the name. However, it does not specify which resources, making it somewhat vague but not a tautology.

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

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

No guidance on when to use this tool versus siblings like 'people' or 'issues'. The description lacks any context about appropriate use cases or alternatives.

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

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

Annotations indicate read-only and non-destructive nature, and the description adds context about data sources (SEC EDGAR + XBRL), scope (US public companies), return value structure (verdict, citation, etc.), and efficiency (replaces sequential calls). 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 moderately concise and front-loaded with the primary purpose. Each sentence adds meaningful information, though it could be slightly shortened by removing redundant phrases while retaining clarity.

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

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

Given the tool's simplicity (single parameter, no output schema), the description is fully complete. It explains the return value elements (verdict, structured form, actual value, citation, delta) and the tool's efficiency advantage, leaving no need for additional 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 description coverage is 100%, so baseline is 3. The parameter description in the schema is already clear and includes examples. The tool's main description adds domain context but does not provide additional parameter-level semantics beyond what the schema offers.

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

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

The description clearly states the tool's function: fact-checking and validating factual claims, specifically for company-financial data. It uses specific verbs like 'fact-check', 'verify', 'validate', and identifies the resource (SEC EDGAR+XBRL). It distinguishes itself from siblings by specializing in financial claims.

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

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

The description explicitly says when to use the tool ('when an agent needs to check whether something a user said is true') and provides example phrasings. However, it does not mention when not to use it or list alternative tools, which would further clarify its role.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, indicating safe read. The description adds no new behavioral details (e.g., data source, response format, or domain context). It does not contradict annotations.

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

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

While very short, the description is underspecified rather than concise. It lacks a clear verb, domain context, or any structuring (e.g., front-loading key info). Every sentence does not earn its place because it provides minimal value.

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

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

Given no output schema, the description should clarify what 'detail' entails (e.g., fields returned). It also fails to differentiate from siblings. The tool is complex enough (single resource retrieval) that a minimal description is inadequate.

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

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

The input schema has one parameter 'id' with no description and 0% schema description coverage. The description 'Volume detail' fails to explain what the ID represents or the expected format. No parameter meaning is conveyed.

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 'Volume detail' is a noun phrase, not a verb+resource. It vaguely suggests retrieving details but does not clearly state the action (e.g., 'get volume details' or 'retrieve a volume by ID'). It does not distinguish from the sibling 'volumes' 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?

No guidance is provided on when to use this tool versus the sibling 'volumes' tool or other tools. The agent is left to infer the difference between singular vs plural, which is ambiguous without context.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds no behavioral context beyond 'list', but does not contradict annotations. However, it could mention typical behavior like pagination or filtering implications.

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

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

The description is extremely short (two words) but this is under-specification rather than effective conciseness. It lacks necessary detail, so while there is no fluff, the brevity harms clarity.

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

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

Given the tool's 4 parameters and lack of output schema, the description is incomplete. It does not clarify parameter relationships (e.g., sort field names, offset limits), response format, or any constraints, leaving significant gaps 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?

With only 25% schema description coverage (only 'filter' has a description), the description adds no additional meaning. It does not explain 'sort', 'limit', or 'offset', leaving the agent to guess their semantics and valid 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?

The description 'Volume list.' states the verb 'list' and resource 'volumes', which is clear but minimal. It does not differentiate from sibling tool 'volume', leaving ambiguity about whether this lists all volumes or has specific scoping.

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

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

No guidance on when to use this tool versus alternatives like 'volume'. No context on prerequisites or common use cases, leaving the agent to infer from the name alone.

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