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

Annotations declare safe read (readOnlyHint), external source usage (openWorldHint), and no destruction. The description adds rich context: routes questions among 1,423+ tools, returns structured answers with stable citation URIs. It discloses the routing mechanism and source scope, going well beyond what annotations provide.

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

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

The description is a single dense paragraph that front-loads the key instruction ('PREFER OVER WEB SEARCH') and then elaborates with examples and capabilities. It could be slightly more concise by removing redundancy (e.g., 'even if web search could also answer it' might be implicit), but overall it efficiently conveys necessary 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 no output schema, the description adequately explains what the agent receives: structured answer with stable citation URIs. It covers input format (natural language question), the scope of sources (1,423+ tools across 392+ verified sources), and usage guidance. For a general-purpose query tool, this is comprehensive.

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 schema description 'Your question or request in natural language' – 100% coverage. The description already explains the tool's purpose, so the parameter meaning is obvious. No added nuance needed, but it doesn't add extra guidance beyond schema.

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

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

The description clearly states it answers factual questions using structured data from authoritative sources, giving specific examples (SEC filings, FDA data, etc.) and positioning itself as preferred over web search for such queries. It effectively distinguishes itself from siblings by emphasizing its breadth of tools and citation URIs.

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

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

Explicit guidance: 'PREFER OVER WEB SEARCH' with detailed conditions (structured data, citations). It lists query types ('what is', 'look up', 'find') and concrete examples, and even says to use it 'even if web search could also answer it.' This leaves no ambiguity about when to invoke 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 indicate readOnlyHint=true and destructiveHint=false. The description adds that the tool resolves markets, classifies bets, fans out to relevant packs (with examples like crypto+fred+gdelt), and returns an evidence packet plus market-vs-model comparison. It does not contradict annotations, though it could mention potential rate limits or response size.

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 dense paragraph that front-loads the main action. It could be slightly more structured (e.g., bullet points), but every sentence earns its place. Some marketing language ('core demo product') is slightly extraneous but not harmful.

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

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

Despite no output schema, the description explains the return value as an 'evidence packet plus a simple market-vs-model comparison'. For a complex tool with multiple data sources, this is sufficient for an agent to understand what it provides. It does not detail the evidence packet structure but covers core functionality.

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 value by clarifying acceptable input formats for 'market' and stating that 'depth' defaults to 'thorough' (not specified in schema). It also explains the automatic classification and fan-out, which guides parameter use.

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

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

The description clearly states the tool researches a Polymarket bet by pulling Pipeworx data, with specific input examples (slug, URL, question text). It distinguishes from siblings like 'polymarket_edges' and 'ask_pipeworx' by emphasizing the comprehensive fan-out and market-vs-model comparison.

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

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

Explicitly states when to use: for 'should I bet on X?', 'what does the data say about this Polymarket market?', or 'is there edge in this bet?'. The final sentence positions it as superior to manual discovery, giving clear alternatives and 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 indicate read-only, open-world, non-destructive. The description adds value by specifying data sources (SEC EDGAR/XBRL, FAERS), return format (paired data + citation URIs), and scope (2–5 entities). 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?

Moderate length, front-loaded with purpose, then usage, then details. Each sentence adds value without redundancy. Could be slightly more concise, but overall well-structured.

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

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

Given the tool's complexity (2 parameters, no output schema), the description is complete: it explains input formats, data sources, return content, and even notes efficiency gain ('replaces 8–15 agent calls'). 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 has 100% coverage with descriptions for both parameters. The description further clarifies expected input formats: tickers/CIKs for company, drug names for drug, with examples. This adds meaningful guidance 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 starts with a specific verb and resource: 'Compare 2–5 companies (or drugs) side by side in one call.' It clearly distinguishes from sibling tools like entity_profile by emphasizing multi-entity comparison and efficiency.

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 trigger phrases ('compare X and Y', 'X vs Y', etc.) and use cases (tables/rankings of financial metrics or drug data). Does not explicitly exclude scenarios, 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 read-only and non-destructive; description confirms it is a discovery tool, explains return format (top-N tools with names+descriptions), and lists domains. No contradiction.

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

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

Description is well-structured: starts with purpose, then usage guidance, then domain list. No wasted words, though slightly long but justified by content.

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

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

Even without output schema, description adequately explains what is returned (top-N tools with names+descriptions) and lists relevant domains. Sufficient for agent to understand tool's behavior.

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

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

Schema coverage is 100% with descriptions for both parameters. Description adds no additional detail beyond schema for parameters; it only includes domain examples and usage tip.

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 tools by describing data/task, uses specific verbs like 'browse, search, look up, discover', and distinguishes itself as the first call to see option set.

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

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

Explicitly says 'Call this FIRST' when many tools are available, and provides context for when to use (browse/search). Does not explicitly mention when not to use, but implication is clear if you know the exact 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 readOnly and non-destructive; description adds detailed return content (SEC filings, fundamentals, patents, news, LEI) and citation format. No contradictions. No rate limit or auth info but acceptable.

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

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

Three sentences, front-loaded with core purpose, no filler. Every clause 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?

Covers input constraints, return types, and future plans. No output schema, but description adequately explains outputs. 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?

Schema coverage is 100% but description adds crucial context: 'type' limited to 'company', 'value' accepts ticker or CIK but not names. This clarifies usage beyond 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?

Description starts with 'Get everything about a company in one call,' clearly stating the verb and resource. It enumerates specific use cases ('tell me about X') and distinguishes from needing multiple other tools.

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

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

Explicitly states when to use (user asks for company profile) and when not (if only have a name, use resolve_entity first). Implicitly suggests it replaces 10+ pack tools.

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

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

Annotations already set destructiveHint: true, so the description need not restate destructiveness. It adds value by explaining the context for deletion (e.g., clearing sensitive data), which is consistent and helpful.

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

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

The description is two concise sentences with no wasted words. The primary action is front-loaded in the first sentence, making it immediately clear what the tool does.

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, no output schema, and annotations covering safety, the description fully covers the necessary context, including when to use it and complementary 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?

The input schema covers the single required 'key' parameter with description 'Memory key to delete.' Since schema description coverage is 100%, the description adds no additional meaning beyond what the schema provides.

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

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

The description states 'Delete a previously stored memory by key.' This is a specific verb+resource combination that clearly distinguishes it from sibling tools like 'remember' and 'recall'.

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

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

The description explicitly provides when-to-use scenarios: 'when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier.' It also recommends pairing with 'remember' and 'recall', giving clear guidance on 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=false, destructiveHint=false) are present and consistent. Description adds behavioral context: team reads digests daily, signal affects roadmap, and it's free. 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?

Concise, front-loaded with purpose. Each sentence adds value. No redundant information. Efficiently communicates all necessary aspects.

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 feedback tool with no output schema, the description is fully complete: it covers purpose, usage, parameters, behavior, and constraints. An agent has all information needed to decide when and how to invoke 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% with detailed descriptions. The description adds guidance on message style ('describe in terms of Pipeworx tools/packs') and character limit (2000), which goes beyond the schema. Baseline 3, plus extra value.

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

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

The description clearly states the tool's purpose: to submit feedback about bugs, missing features, or praise. It distinguishes itself from sibling tools (e.g., ask_pipeworx, discover_tools) by specifying that it's for reporting issues or suggestions, not for queries or discovery.

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 describes when to use (bug, feature gap, praise) and what not to do (don't paste end-user prompt). Includes rate limit (5/day) and cost (free, no quota). Provides context for alternatives implicitly by contrasting with other tool purposes.

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

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

Annotations (readOnlyHint, openWorldHint, destructiveHint) are present and consistent. The description adds valuable behavioral context, detailing the algorithmic steps (walking child markets, searching across events, grouping, checking monotonicity) and output format (ranked opportunities with direction and reasoning).

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 (about 150 words) and well-structured, starting with the core purpose followed by clear bullet-point modes. Every sentence provides essential information without redundancy.

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

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

Despite no output schema, the description sufficiently explains the return value (ranked opportunities with suggested trade direction and reasoning). Given the tool's complexity (two modes, cross-event searching), the description covers all necessary aspects 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%, so baseline is 3. The description adds meaning beyond the schema by providing concrete examples (e.g., 'when-will-bitcoin-hit-150k', 'Strait of Hormuz traffic returns to normal') and clarifies the expected input for each mode.

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

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

The description clearly states the tool's purpose: finding arbitrage opportunities on Polymarket via monotonicity violations. It distinguishes two modes (event and topic) and differentiates from siblings by specifying its unique functionality.

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

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

Explicit guidance on when to use each mode is provided, including an example where topic mode catches cross-event cases that event mode misses. However, no explicit exclusions or alternatives to sibling tools are 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?

Description adds behavioral context beyond annotations: explains the model ('lognormal model from FRED + live coinpaprika price'), the process (scans top markets, groups by asset, computes model probability), and that it's read-only. 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, front-loaded with purpose. Every sentence adds value. Could be slightly more concise but 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?

Despite no output schema, description describes return format (top N ranked by edge magnitude with trade direction). Covers algorithm and purpose adequately for a discovery tool.

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

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

Schema coverage is 100%, so baseline 3. Description does not add new parameter semantics beyond defaults and max values, which are already in schema. No extra meaning provided.

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

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

The description uses specific verbs ('Scan', 'return') and resource ('Polymarket markets', 'Pipeworx data'), clearly distinguishing from siblings like 'polymarket_arbitrage'. It explicitly states the goal: find markets where data disagrees with market price.

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

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

The description explicitly states the use case ('what should I bet on today') and implicit when-not-to-use (if you need per-market analysis, use other tools). Sibling differentiation is implied but not explicit.

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, so the description's 'Pull data' aligns. However, the description does not elaborate on behavior beyond what annotations cover, such as expected return format, timeouts, or error states. It adds minimal context about the query format.

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

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

The description is extremely concise with two short sentences. It is front-loaded with the action. However, it sacrifices completeness for brevity; a slightly longer description could convey more 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 the tool's complexity (nested query parameter, no output schema), the description is too minimal. It lacks information about expected output format, credential requirements, or how to construct a valid query. For a data retrieval tool, more context is needed to enable effective 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?

Schema description coverage is 50%: only 'body' has a description showing its structure. The description repeats that 'body is a PxWeb query object', which adds some clarity but does not explain the 'path' parameter at all. With a complex nested object and an undocumented parameter, more parameter-level guidance is needed.

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 pulls data from a table, which is straightforward. It specifically mentions the body parameter is a PxWeb query object, giving a hint about the API. However, it does not explicitly differentiate from sibling tool 'table_meta', which likely handles metadata 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?

No guidance is provided on when to use this tool versus alternatives like 'table_meta'. There is no mention of prerequisites, typical use cases, or scenarios where this tool is preferred. The description is purely operational without contextual advice.

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 scoping to identifier and pairing with remember/forget. No additional critical behavioral details needed beyond annotations.

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

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

Two sentences, no extraneous information. Purpose, usage, and scope are front-loaded 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?

Tool is simple with one optional parameter and no output schema. Description covers purpose, usage, scoping, and complementary tools adequately. No gaps identified.

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

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

Schema description for 'key' is present, but description adds meaningful context: omitting key lists all saved keys. This adds value beyond the schema, justifying a score above baseline 3.

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

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

Description clearly states retrieval of previously saved values via remember, and listing all keys if argument omitted. Differentiates from siblings remember and forget by specifying the complementary 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?

Provides explicit usage context (look up stored context) and behavior when key is omitted. Could be more precise about when not to use, but sufficiently guides agent for this simple 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 and destructiveHint=false. The description adds significant behavioral details: parallel fan-out to three sources, supported date formats (ISO and relative), and return structure (changes, count, 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 concise (4-5 sentences), front-loaded with purpose, and uses well-structured prose. Every sentence adds information without redundancy.

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

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

For a tool with 3 required params and no output schema, the description fully compensates by explaining the return format (structured changes, total count, URIs) and data sources. It is complete for effective use.

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

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

Schema coverage is 100%, but the description adds value by explaining date format options with examples (e.g., '30d' for monitoring) and clarifying that value can be a ticker or CIK. This enhances understanding 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 finds recent changes about a company within a time window, with example queries like 'what's happening with X?' and 'any updates on Y?'. It distinguishes itself from siblings by explicitly listing the data sources (SEC EDGAR, GDELT, USPTO) and the output format.

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 example queries and mentions monitoring use cases. Does not explicitly state when not to use or list alternatives, but the context is clear enough for an agent to select this tool for change-related queries.

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

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

Adds valuable behavioral context beyond annotations: scoped by identifier, persistent for authenticated users, 24-hour retention for anonymous. Annotations are minimal (readOnlyHint=false, destructiveHint=false) and the description 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?

Concise at 4 sentences, front-loaded with purpose, no redundant information. Each sentence contributes to understanding.

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?

Comprehensive for a write tool: covers purpose, usage, behavioral details, and parameter semantics. No output schema exists, but the description does not need to explain return values for this tool.

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

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

Input schema covers both parameters with descriptions. The description adds value by providing examples for keys (e.g., 'subject_property', 'target_ticker') and clarifying that value is any text, enriching 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's purpose: 'Save data the agent will need to reuse later'. It specifies the verb (save) and resource (data), and distinguishes itself from siblings like 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 provides guidance on when to use: 'Use when you discover something worth carrying forward... so you don't have to look it up again.' It also explains pairing with recall and forget, and gives concrete examples (ticker, address, preferences).

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 the safety profile. The description adds value by specifying that the tool returns IDs plus pipeworx:// citation URIs, and mentions it replaces multiple lookup calls, which is useful 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 approximately 80 words in two short paragraphs, with the core purpose stated first. Every sentence adds value: purpose, illustrative examples, usage instruction, and efficiency claim. There is no redundancy or fluff.

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

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

Given the tool's simplicity (2 parameters, no output schema, clear annotations), the description covers purpose, usage guidance, return content, and integration advice. It might lack explicit output structure details, but the examples mitigate this. Overall, it provides sufficient context for an agent to 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 coverage is 100%, so the baseline is 3. The description enhances parameter understanding with concrete examples (e.g., 'Apple' → AAPL/CIK, 'Ozempic' → RxCUI) and restates the accepted formats for the 'value' parameter, providing extra clarity beyond the schema descriptions.

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

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

The description clearly identifies the tool's purpose: resolving user-provided names into canonical identifiers (CIK, ticker, RxCUI, LEI). It provides concrete examples (Apple, Ozempic) and explicitly distinguishes its role from sibling tools by stating it should be used before other tools that need official IDs and that it replaces multiple lookups.

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

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

The description states exactly when to use the tool: when a user mentions a name and you need a canonical identifier. It advises to use it before calling other tools that require such IDs. Although it does not explicitly list alternatives or exclusions, the context is clear and sufficient 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 declare read-only and non-destructive behavior. The description adds little beyond confirming navigation, but does not contradict annotations.

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

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

The description is very concise at four words, but it is so terse that it lacks necessary context. It is not overly verbose, but could be more informative while remaining 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 simplicity of the tool (one parameter, no output schema), the description fails to explain what the subject tree is or what navigating entails. It is incomplete for a clear understanding.

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

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

Schema description coverage is 100% for the single parameter path, which already explains its meaning. The description does not add any additional parameter details 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 states the tool navigates a subject tree, which provides a verb and resource. However, it lacks specificity about what subjects are and does not distinguish this tool from siblings like query_table or resolve_entity.

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 given on when to use this tool versus alternatives, nor are there any prerequisites or typical use cases 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=true and destructiveHint=false. The description adds that it returns dimensions and valid values, but doesn't disclose additional behavioral traits (e.g., whether it lists all tables or just one, or if it has 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?

The description is very concise (8 words), which is efficient. However, it lacks front-loaded key terms and could benefit from a clearer structure like 'Gets the definition of a table, including dimensions and valid values.'

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 one-parameter tool with no output schema, the description gives some idea of what is returned (dimensions, valid values) but doesn't specify the format or whether it returns schema for all tables or just one. More completeness would help an agent understand the output.

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

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

Input schema has 100% coverage with a description and example for the single parameter 'path'. The description adds no new information about the parameter 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 'Table definition (dimensions, valid values)' indicates the tool returns metadata about a table, but it's vague about the exact action (e.g., 'get' or 'describe'). It somewhat distinguishes from sibling 'query_table' which queries data, but not 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?

No guidance on when to use this tool versus alternatives like 'query_table' or 'discover_tools'. The description lacks any context for when a user should choose this tool over others.

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, openWorldHint) and adds valuable behavioral context: it performs NL parsing, entity resolution, data lookup, and numeric comparison. It details the return value (verdict types, citation, percent delta). 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 reasonably concise but packed with essential information. It front-loads the primary action and provides structured output details. Could be slightly tighter, but no unnecessary 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 single parameter and no output schema, the description adequately covers the claim types supported, the underlying data sources, the verdict options, and the efficiency gain. It is complete for an agent to understand invocation and expected results.

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

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

The single parameter 'claim' has comprehensive description in both schema and description. Schema coverage is 100%, and the description elaborates with concrete examples of valid claims, adding meaning 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's function: fact-check, verify, validate claims. It provides concrete use examples ('Is it true that...?') and specifies the domain (company-financial claims via SEC EDGAR). It distinguishes itself by noting it replaces multiple sequential calls, setting it apart from sibling tools like ask_pipeworx or 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 explicitly states when to use the tool (checking factual claims) and gives example queries. It also sets boundaries by noting v1 supports only company-financial claims. However, it does not explicitly state when not to use it or mention alternative tools for non-financial claims.

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