Devdocs Io

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

The description reveals that the tool dispatches questions to internal tools, fills arguments, and returns citations. This adds value beyond annotations, which already indicate read-only and non-destructive behavior. However, it does not specify error handling or limits, so not a 5.

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

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

The description is a single paragraph that is front-loaded with the key directive to prefer over web search. It efficiently lists example data types and usage patterns. It could be slightly shorter, but 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?

Given the tool's complexity (many sources, no output schema), the description adequately covers purpose, usage, behavior, and return format (structured answer with citations). Missing details like error messages or rate limits, but annotations cover safety. Overall, highly informative.

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

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

Only one parameter ('question') with 100% schema coverage. The description explains that the question will be routed to the appropriate tool and its arguments filled, which adds context beyond the schema's 'natural language' description. A 5 would require more detail on natural language syntax or constraints.

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

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

The description explicitly states the tool's purpose: to answer factual questions by routing them to one of 2,520 tools across 575 sources, returning structured answers with citations. It clearly distinguishes from web search and provides specific examples of data types covered.

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 explicit guidance to prefer this tool over web search for factual questions. It lists concrete use cases ('what is', 'look up', etc.) and provides examples like 'current US unemployment rate' and 'Apple's latest 10-K', making it clear when 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 declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds behavioral detail: it resolves the market, classifies the bet, fans out to relevant packs, and returns an evidence packet with 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 moderately concise (4 sentences) and each sentence adds value. It is front-loaded with the core action and well-structured, but could be slightly trimmed.

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

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

Despite no output schema, the description adequately explains the return value (evidence packet + market-vs-model comparison). The tool is complex with many siblings, and the description provides enough context to differentiate it.

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 some context (e.g., default depth is 'thorough') but the schema already describes both parameters adequately. The description does not significantly extend parameter 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 that the tool researches a Polymarket bet by pulling Pipeworx data, and specifies inputs (slug, URL, question text) and outputs (evidence packet plus market-vs-model comparison). It distinguishes itself from other tools by focusing on aggregated bet research.

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

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

The description provides clear use cases ('should I bet on X?', 'what does the data say?') and implies when to use it (for bet-specific context). However, it does not explicitly mention when not to use it or list alternatives like similar 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 declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. Description adds behavioral context: data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), return format (paired data + 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, efficiently front-loaded with purpose. Every sentence provides value, though the description could be slightly more structured (e.g., separate lines for company vs drug specifics). Still compact 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?

No output schema, but description explains return type generically ('paired data + pipeworx:// citation URIs'). Given the tool's complexity (two entity types, multiple metrics), the description is fairly complete, though exact output structure could be elaborated.

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%. Description adds meaning beyond schema: explains type enum values ('company' pulls financials, 'drug' pulls adverse events) and gives concrete examples for values (tickers like 'AAPL' for companies, drug names like 'ozempic').

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

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

The description clearly states it compares 2–5 companies or drugs side by side, with specific verb ('compare') and resource ('entities'). It distinguishes between company and drug types, and lists concrete use cases like 'compare X and Y' or 'X vs Y'.

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 triggers (user phrases like 'compare X and Y') and notes this tool replaces 8–15 sequential agent calls, implying efficiency. However, it does not explicitly state when not to use it or mention alternatives (e.g., entity_profile for single entities).

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description only adds 'for a doc (large)', hinting at large data but not explaining behavior like pagination, data format, or external dependencies. Minimal added value.

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

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

The description is very short (one sentence) and concise, but it lacks necessary structure and content. Front-loading is minimal due to vagueness. Conciseness is not a strength when the content is insufficient.

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

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

For a simple tool with one parameter and no output schema, the description is incomplete. It does not explain what the returned 'content database' contains, how to interpret results, or any size limitations. The agent lacks essential context for correct usage.

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

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

The input schema has one required parameter 'slug' with no description. The tool description does not explain what 'slug' represents or its role, despite 0% schema description coverage. The agent is left guessing the parameter's 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 'Content database for a doc (large)' lacks a verb and does not explicitly state what action the tool performs (e.g., fetch, retrieve). It describes the resource but not the operation, leaving the purpose vague. Sibling tools like 'docs' and 'search_docs' have clearer purposes.

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 'docs' or 'search_docs'. The description gives no context about the tool's niche or limitations.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description adds useful context: it returns top-N relevant tool names and descriptions. No contradictions. It does not detail internal ranking, but that is acceptable for a discovery tool.

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

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

The description is a single paragraph that front-loads the purpose ('Find tools by describing the data or task'). The list of domains is a bit long but useful for the agent. Overall concise without wasted sentences.

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

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

Given the tool's simplicity (two params, no output schema), the description covers purpose, usage, and return format ('names + descriptions'). It lacks detail on result ranking but is sufficient for an agent to use it 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?

Schema coverage is 100% with descriptions for both query and limit. The description adds 'top-N most relevant tools' context but does not significantly enhance understanding 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 'Find tools by describing the data or task' and lists specific domains (SEC filings, FDA drugs, etc.), distinguishing itself from sibling tools that directly access data. It is a specific verb+resource combination.

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 when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available and want to see the option set.' While it does not explicitly exclude alternatives, it provides clear context for 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 already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, covering safety and scope. The description adds 'Full docs index', implying it returns all documents, but does not elaborate on behavior (e.g., pagination, format). With annotations present, a score of 3 is appropriate.

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

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

The description is very concise (3 words), front-loading the core purpose. Given the tool's simplicity (no parameters), this length is appropriate with no wasted 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?

The description states the tool returns the full docs index but provides no details about what that index contains or how to interpret results. With no output schema, additional context would be beneficial, but the tool is simple enough that this is minimally adequate.

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 no parameters and 100% schema description coverage. Since there are no parameters to document, the description does not need to add parameter semantics. Baseline of 4 is suitable.

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 'Full docs index' clearly indicates this tool returns the entire index of documents. It differentiates from siblings like 'search_docs' (which implies filtering) and 'index' (which might be a listing tool). However, it lacks a verb, relying on the noun phrase to convey action.

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

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

No usage guidelines are provided. The description does not specify when to use this tool over alternatives like 'search_docs' or 'index', nor does it mention any context or prerequisites.

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. Description adds valuable context: it returns recent SEC filings, fundamentals, patents, news, and LEI with pipeworx:// citation URIs, making behavior fully transparent.

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

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

Concise yet comprehensive: ~6 sentences, front-loaded with purpose, no fluff. Every sentence adds value, from use cases to output details to parameter restrictions.

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 enumerates what is returned (SEC filings, fundamentals, patents, news, LEI). It covers the tool's complexity (aggregating 10+ data sources) and provides complete context for 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. Description adds extra meaning: 'value' must be ticker or CIK, and explicitly states 'names not supported — use resolve_entity first', which is critical guidance not present in schema.

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

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

The description clearly states the tool's purpose: 'Get everything about a company in one call.' It specifies when to use it (e.g., user asks for company info) and what it returns, distinguishing it from multiple sibling tools.

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

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

Explicitly lists when to use (user queries like 'tell me about X') and when not to (names not supported, must use resolve_entity first). Also clarifies only 'company' type is supported, guiding correct invocation.

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 no behavioral context beyond stating 'HTML' output.

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 uninformative; not a concise description but an under-specification.

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

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

No output schema and no parameter details; description leaves agent uninformed about inputs and outputs.

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

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

Schema has 0% coverage (no parameter descriptions); description fails to explain what 'path' or 'slug' mean or how they are used.

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 'Single entry HTML.' is vague; lacks verb to specify action (e.g., retrieve, render) and does not distinguish from sibling tools like 'db' or 'docs'.

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 instead of alternatives; no 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 destructiveHint=true and readOnlyHint=false. Description confirms delete operation and adds context about clearing sensitive data. No contradictions, but does not disclose error behavior for missing keys.

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 immediately convey purpose and usage. No extra words. Front-loaded with verb and resource.

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?

Simple tool with 1 parameter fully covered by schema. Description provides purpose, usage context, and sibling relationships. No missing information for 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 covers 100% of parameters with description for 'key'. Description adds 'by key' but no additional meaning beyond 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?

Description clearly states action 'delete' and resource 'previously stored memory by key'. It distinguishes from siblings 'remember' (store) and 'recall' (retrieve), making purpose unambiguous.

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

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

Explicitly provides when to use (stale context, task done, clear sensitive data) and pairs with 'remember' and 'recall'. No guidance on when not to use, but given simplicity it's 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 (readOnlyHint, openWorldHint) already indicate safe read operation, but description adds no behavioral details such as what an 'index' contains, how entries are listed, or any pagination/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?

While short (one sentence), it fails to be informative. The sentence does not earn its place because it adds no actionable guidance.

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

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

Given the tool has 1 required parameter and no output schema, the description should clarify what the tool returns (e.g., list of entries, IDs, metadata). It is insufficient for an agent to invoke correctly.

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

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

Schema description coverage is 0% and description does not explain the 'slug' parameter. Agent cannot infer whether slug refers to document ID, title, or path.

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 a noun phrase 'Index of entries inside a doc' rather than a verb+resource statement. It does not clearly specify the action (e.g., lists, retrieves). Siblings like 'entry' and 'search_index' have distinct names but description does not clarify what this tool does relative to them.

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

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

No guidance on when to use this tool versus siblings such as 'entry', 'search_index', or 'docs'. The description lacks context about appropriate scenarios or prerequisites.

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 adds significant behavioral context beyond annotations: confirms it's free, not counted against quota, rate-limited to 5 per day, and that the team reads digests daily. 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?

Description is relatively concise (4 sentences) and front-loaded with purpose. Could be slightly tighter but 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?

Given no output schema and nested parameters, the description fully explains the tool's purpose, when to use, parameter details, rate limits, and expected 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 covers all parameters with descriptions. Description adds extra guidance on how to phrase the message (in terms of tools/packs) and reinforces the type enum, providing value beyond schema.

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

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

Description clearly states the tool is for providing feedback about bugs, missing features, etc., and distinguishes it from other tools by emphasizing it's about reporting issues to the Pipeworx team, not fetching data.

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

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

Explicitly specifies when to use (e.g., bug, feature gap, praise) and what not to do (avoid pasting end-user prompts), plus mentions rate limits and team consumption.

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, destructiveHint=false, so the tool is safe. The description adds substantial behavioral context: it walks child markets, groups related markets, checks monotonicity, and returns ranked opportunities with suggested trade direction and reasoning. This goes beyond annotations to explain internal logic and output.

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 two clearly labeled modes, front-loaded with the main purpose. Every sentence adds value, including examples and edge cases. It is appropriately sized for the complexity of the tool, with 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 the complexity (two modes, cross-event search, monotonicity checking) and no output schema, the description covers purpose, modes, parameters, internal logic, and return format (ranked opportunities with reasoning). It is fully self-contained and provides everything an agent needs to decide 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?

The input schema has 100% coverage with descriptions for both 'event' and 'topic' parameters. The description adds significant meaning by explaining the two modes, providing examples (e.g., 'when-will-bitcoin-hit-150k' for event, 'Strait of Hormuz traffic returns to normal' for topic), and clarifying how each parameter is used and what the tool does with them.

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 on Polymarket, distinguishing two modes ('event' for a single event, 'topic' for cross-event). It specifies the verb (find), resource (arbitrage opportunities), and scope (Polymarket related markets), making it highly clear and distinct 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 explains when to use each mode: 'event' for a single Polymarket event slug, 'topic' for cross-event related markets. It provides a concrete example (May≤June rule) to illustrate when the cross-event mode is necessary, effectively guiding the agent on appropriate usage without needing to mention alternatives.

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

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

The annotations already indicate readOnlyHint and openWorldHint, and the description adds valuable behavioral context: it explains the model (lognormal from FRED + coinpaprika price), the process (scans top markets, groups by asset, fetches price history once, computes probability, ranks by edge), and the output format. This goes beyond annotations and gives the agent a clear understanding of the tool's 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 a single paragraph that front-loads the purpose and then details the methodology. It is reasonably concise given the complexity, though it includes slightly verbose details like 'V1 covers crypto-price bets (lognormal model from FRED + live coinpaprika price)' which could be trimmed.

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 three parameters, no output schema, and no nested objects, the description covers the core functionality well: it explains the input parameters, the algorithmic process, and the output (top N edges with trade direction). It does not provide error examples or return format details, but the description adequately prepares an agent to use the tool.

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

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

Schema description coverage is 100%, so the schema already documents all parameters. The description reiterates defaults (limit default 10, max 25; window default 1wk; min_edge_pp default 0.5) but adds no new meaning beyond what is in the schema.

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

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

The description explicitly states the tool scans high-volume Polymarket markets to find where Pipeworx data disagrees with market price, returning top N edges with suggested trade direction. It includes the specific use case 'what should I bet on today', making the purpose highly clear and distinct from siblings like 'polymarket_arbitrage'.

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

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

The description provides clear context for when to use the tool: 'agents/users discover opportunities without paging through hundreds of markets by hand.' It frames the tool as a solution for the 'what should I bet on today' question. However, it does not explicitly list when not to use it or compare with alternative 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 declare read-only, non-destructive. Description adds valuable context: scoped to identifier, pairing with other tools. No contradictions.

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

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

Three focused sentences, front-loaded with main action. No redundancy, though minor wordiness in middle sentence.

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 tool with full schema coverage and no required parameters, description fully equips 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 covers 100% with key parameter description. Description adds behavior: omit key to list all keys, which enhances understanding.

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

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

Description clearly states tool retrieves values saved via remember or lists keys, with specific verb+resource and examples. Distinguishes from siblings remember and forget.

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

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

Explicitly states when to use (look up stored context) and implies pairing with remember/forget. Does not explicitly state when not to use, but context is clear.

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

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

Annotations already indicate readOnly=true and non-destructive. The description adds that it fans out to SEC EDGAR, GDELT, and USPTO in parallel and returns structured changes with count and URIs. Minor details like error handling are omitted, but overall sufficient.

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 use cases, second details source fans-out and parameter format. Every sentence earns its place with no waste.

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 format (structured changes, total_changes, URIs). Covers all three parameters and behavior. Complete for a monitoring tool given good annotations.

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

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

Schema coverage is 100% with descriptions. The description adds context: since accepts ISO dates or relative shorthand ('7d', '30d'), value accepts ticker or CIK, and type is limited to 'company'. Provides concrete examples like 'AAPL' and '0000320193'.

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' and gives specific query examples. It distinguishes from siblings like entity_profile (static) by emphasizing recent changes and parallel fan-out to multiple sources.

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

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

Explicitly lists example user queries and when to use. Also implies alternatives via sibling list (e.g., entity_profile for static data) and provides context for typical monitoring intervals like '30d'.

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

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

Discloses key behavioral traits beyond annotations: scoped by identifier, persistent vs. 24-hour retention based on authentication, and key-value storage. 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?

Description is concise with every sentence adding value: purpose, examples, scoping, retention, and complementary tools. 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?

For a simple key-value store tool with no output schema, the description is fully complete. It covers purpose, usage, behavior, and related tools, leaving no gaps.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds example key names ('subject_property', 'target_ticker') but does not significantly enhance understanding beyond what the schema already provides.

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

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

The description clearly states the tool's verb ('Save') and resource ('data'), provides concrete examples (ticker, address, preference), and distinguishes from siblings by naming recall and forget.

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

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

Explicitly says 'Use when you discover something worth carrying forward' and instructs to pair with recall/forget, giving clear guidance on 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?

Annotations (readOnlyHint=true, openWorldHint=true, destructiveHint=false) already declare it non-destructive and read-only. The description adds valuable context: returns IDs plus citation URIs, and mentions the ID systems covered. No contradictions.

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

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

The description is front-loaded with the core purpose and includes multiple sentences, each contributing useful information (purpose, usage, examples, output). While efficient, it could be slightly trimmed; however, it remains effective 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?

Given two parameters, no output schema, and complete annotations, the description adequately explains input, output (IDs + URIs), use case, and temporal placement. It covers all necessary context for an agent to use the tool correctly.

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

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

Schema coverage is 100%, but the description adds significant value by explaining acceptable formats for the 'value' parameter (ticker, CIK, name for companies; brand/generic for drugs) and providing clear examples. This goes beyond the schema's minimal descriptions.

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

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

The description clearly states the tool resolves names into official identifiers (CIK, ticker, RxCUI, LEI) for companies or drugs. Examples like 'Apple' and 'Ozempic' make it concrete. It distinguishes itself from siblings by claiming it replaces multiple lookup calls, implying it is the primary identification 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 advises 'Use this BEFORE calling other tools that need official identifiers,' providing clear when-to-use guidance. It does not explicitly state when not to use or list alternative tools, but the context (e.g., need for specific ID systems) implies 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=true and destructiveHint=false, so the tool is safe. The description adds the behavioral detail 'by substring', but lacks information about result format, pagination, or limits, which the annotations do not cover.

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

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

The description is a single, front-loaded sentence with no extraneous words, achieving maximum 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?

For a simple search tool with one parameter and no output schema, the description is minimally adequate. It lacks details on case sensitivity, match type (exact vs. partial), order of results, or expected output structure, but the tool's simplicity partially compensates.

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

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

With 0% schema description coverage, the description adds the semantic detail 'substring' to the lone parameter 'query', providing essential context beyond the bare schema. However, it remains minimal and does not clarify format or 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?

The description clearly states the verb 'filter' and the resource 'docs index', indicating it performs substring matching. It is specific enough to differentiate from siblings like 'search_index' or 'db', though not explicitly referencing them.

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

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

No guidance is provided on when to use this tool versus alternatives (e.g., 'search_index', 'db'), nor are there any prerequisites or exclusions mentioned.

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, and destructiveHint. The description adds 'substring-search' but lacks details on pagination, case sensitivity, or other behaviors. It provides modest additional context.

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

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

The description is a single sentence, which is concise but lacks necessary substance. It is not wasteful but is incomplete.

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

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

With no output schema, no parameter descriptions, and sibling tools, the description does not provide enough context for correct invocation. It does not explain what 'entries' are or how results are returned.

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

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

Schema description coverage is 0%, and the description does not explain any parameters. It omits meaning for 'slug', 'query', and 'limit', failing to compensate for missing schema descriptions.

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

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

The description says 'Substring-search entries inside a doc.' It adds some specificity beyond the name but is vague on what 'entries' are and does not distinguish from sibling tools like 'search_docs'.

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 'search_docs' or 'entry'. There is no mention of 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?

The description aligns with annotations (readOnlyHint=true, destructiveHint=false). It adds minimal context beyond the annotations, simply clarifying that the listing is 'inside a doc'. No additional behavioral traits (e.g., auth needs, pagination) are disclosed.

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 very short and to the point. However, given the lack of parameter details, it sacrifices completeness for brevity. It could be improved by adding a brief note about the 'slug' parameter.

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

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

For a simple tool with one parameter and no output schema, the description is insufficient. It does not explain what constitutes a 'doc', how categories are returned, or any constraints. The agent is left with 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?

The input schema has 0% description coverage, and the description provides no explanation of the 'slug' parameter. The agent has no idea what value to provide for 'slug', hindering correct invocation.

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 'List categories' and the target 'inside a doc'. It is specific enough to convey the tool's primary function, but does not differentiate from sibling tools like 'docs' or 'search_docs', which may operate on similar data.

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

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

No guidance is provided on when to use this tool versus alternatives, nor are there any preconditions or exclusions mentioned. The agent must infer usage solely from the tool name and description.

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 adds that the tool returns a verdict with structured data and a citation, and explains its internal steps (parsing, entity resolution, lookup, comparison). It could mention error handling or latency, but overall it provides good behavioral context beyond annotations.

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

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

The description is only four sentences, each essential. It front-loads the core purpose, then covers when to use, domain specifics, return values, and efficiency benefits. No redundant or extraneous 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 tool's complexity (NL parsing, entity resolution, data lookup, numeric comparison), the description fully explains the domain, claim format, return values (verdict types, actual value, citation, delta), and that it replaces multiple steps. No output schema exists, so the description must cover returns, and it does so thoroughly.

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 single parameter 'claim' has 100% schema description coverage. The description adds examples of natural-language claims and clarifies that it should be a factual statement. This adds value beyond the schema by illustrating acceptable input and the domain.

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: fact-checking natural-language claims against authoritative sources. It specifies the verb (validate) and resource (claims), and distinguishes it from siblings by noting it replaces multiple sequential calls. The domain is explicitly limited to company-financial claims for US public companies.

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 examples. It also specifies limitations ('v1 supports company-financial claims') and contrasts with alternatives ('replaces 4–6 sequential calls').

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