Send That Email

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

The description discloses that the tool automatically picks the right tool and fills arguments, and that no browsing or schema learning is needed. Since no annotations are provided, the description adequately covers behavioral traits like delegation to other tools.

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 (three sentences) and front-loaded with the core purpose. Every sentence adds value: first states action, second explains mechanism, third gives examples.

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

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

Given the tool's simplicity (one parameter, no output schema, no annotations), the description is nearly complete. It explains the tool's functionality and provides examples. The only gap is lack of mention about potential limitations or error handling.

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

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

Schema coverage is 100% for the single required parameter 'question'. The description adds context by specifying that the question should be in natural language and providing examples, but it does not elaborate on constraints or format beyond what the schema's description already states.

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 accepts natural language questions and returns answers, using a single verb 'Ask' and specifying the resource 'pipeworx'. It distinguishes itself from sibling tools by promising automatic tool selection and argument filling.

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

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

The description explains when to use the tool: when you have a question in plain English. It provides examples of appropriate queries. However, it does not explicitly state when not to use it or mention alternatives among siblings.

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

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

Beyond annotations (readOnlyHint, openWorldHint, destructiveHint), the description adds valuable behavioral details: it resolves market, classifies bet type, fans out to appropriate data packs, and returns a market-vs-model comparison. 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 a single paragraph that front-loads the main action and then provides details in a logical flow. It is not overly verbose, though it could be slightly trimmed. 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 only 2 parameters, the description covers what the tool does, what inputs are expected, and what the output includes (evidence packet + comparison). It is complete enough for an agent to use 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%, so the description is not required to add much for parameters. It adds a hint about market parameter (slug, URL, or question) which is already in schema. For depth, no additional detail 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?

The description uses a specific verb 'Research' and a specific resource 'Polymarket bet', clearly differentiating from sibling tools like 'ask_pipeworx' or 'validate_claim'. It states exactly what it does: pulls Pipeworx data, resolves markets, classifies, fans out packs, and returns evidence with 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?

The description explicitly lists use cases: 'should I bet on X?', 'what does the data say?', 'is there edge?'. It provides clear context for when to use, though it does not explicitly mention when not to use or name 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?

With no annotations, the description carries the full burden. It discloses that the tool is a one-call operation, returns paired data and URIs, and specifies data fields per type. However, it lacks details on error handling or limitations.

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

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

The description is concise at two sentences with no wasted words. The first sentence states the core purpose, and the second expands on specifics, making it highly efficient.

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

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

Given the tool's complexity (two entity types with distinct data returns), the description covers all essential aspects: entity count, type-specific fields, and output format (paired data + URIs). No output schema exists, but the description sufficiently explains the return.

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

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

Schema description coverage is 100%, but the description adds significant meaning by explaining the 'values' format per entity type and the returned data fields. This goes beyond the schema's basic type/enum 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 compares 2-5 entities side by side, with specific data returned for 'company' and 'drug' types. It uses a specific verb ('compare') and resource ('entities'), and distinguishes itself from sequential calls.

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 on when to use the tool (for side-by-side comparison) and specifies usage for company vs drug types. It mentions replacing multiple sequential calls, but does not explicitly state when not to use it.

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

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

No annotations are provided, so the description must carry the full burden. It states that it 'Returns the most relevant tools with names and descriptions,' which is a basic behavioral promise. However, it does not disclose potential side effects, rate limits, or whether it's read-only. The description is adequate but lacks depth.

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

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

The description is two sentences: the first states purpose, the second gives usage guidance. Both are essential, no wasted words. Front-loaded with the action verb.

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 params, no output schema, no nested objects), the description covers the key aspects: purpose, when to use, and return type. It could mention the return format or that it returns only names and descriptions, but this is already stated. Slight room for improvement in behavioral details.

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 baseline is 3. The description mentions the query parameter indirectly ('by describing what you need') but does not add new semantics beyond the schema's description of 'query' and 'limit'. No additional value 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 verb+resource: 'Search the Pipeworx tool catalog by describing what you need.' It clearly states the function (search tools) and differentiates from siblings by emphasizing discovery of tools among many, while siblings like ask_pipeworx are for Q&A and send_that_email_analyze for email analysis.

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

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

The description explicitly tells when to use it: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This provides clear guidance on priority and context, setting expectations for the tool's role as an initial discovery step.

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?

No annotations provided, so description carries full burden. It describes the data returned comprehensively and mentions citation URIs. Could explicitly state read-only, but nature implies no side effects.

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?

Very concise: single paragraph front-loaded with main purpose, followed by data listing and efficiency note. No wasted words, every sentence adds value.

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

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

Despite no output schema, description covers all major aspects: inputs, data sources, output format (citation URIs), and limitations (type only company). Complete for a complex tool.

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

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

Schema coverage is 100% so baseline 3. Description adds value: explains that value can be ticker or CIK, and that names require resolve_entity. Clarifies type enum.

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

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

Description clearly states it returns a full profile across multiple packs, listing specific data types (SEC filings, revenue, patents, etc.) and explicitly distinguishes from siblings like 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?

Explicitly states when to use (instead of sequential calls), when not to use (names not supported), and alternatives (resolve_entity). Also clarifies only 'company' type supported.

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?

With no annotations provided, the description carries the full burden. It states the tool deletes a memory, which implies irreversible action, but does not disclose potential side effects, permissions required, or whether deletion is permanent. Acceptable but lacks depth.

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

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

One short sentence, front-loaded with the verb and resource, no wasted words. Perfectly concise for a simple tool.

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

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

Given the low complexity (1 required parameter, no output schema, no nested objects), the description is adequate. It tells the agent exactly what to do. However, adding a note about the irreversible nature or required permissions would make it fully complete.

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

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

Schema description coverage is 100% (the single parameter 'key' is described). The description adds no additional meaning beyond the schema, but baseline is 3 and the simplicity of a single key parameter makes it sufficient. Slight credit for being clear.

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 ('Delete') and the resource ('a stored memory by key'). It is distinct from sibling tools like 'remember' (create) and 'recall' (retrieve), leaving no ambiguity about its purpose.

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

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

The description implies when to use it (when you need to delete a memory) but provides no explicit guidance on when not to use it or alternatives. With sibling tools like 'recall' and 'remember', a brief mention of those would improve this score.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It only reveals the rate limit and that the tool sends data (write operation). It does not specify whether the tool returns a response, requires authentication, or whether feedback is stored or reviewed. This lack of detail on side effects and return behavior is a significant gap.

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

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

The description is three concise sentences, front-loading the purpose and then listing use cases. Every sentence adds value (purpose, usage details, rate limit). No redundant or 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?

For a simple feedback tool with 3 parameters (one nested) and no output schema, the description covers most essential aspects: purpose, usage, message content guidelines, and rate limit. It does not mention what the tool returns (e.g., a confirmation), but that is minor for a feedback submission tool. Overall, it is nearly complete.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaningful guidance beyond the schema, particularly for the message parameter: 'Describe what you tried in terms of Pipeworx tools/data — do not include the end-user's prompt verbatim.' This helps the agent craft appropriate feedback. No other parameter enhancements are 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 explicitly states 'Send feedback to the Pipeworx team' and lists specific use cases: bug reports, feature requests, missing data, praise. This clearly distinguishes it from sibling tools like ask_pipeworx (for queries) or compare_entities (for comparisons).

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 when-to-use guidance: 'Use for bug reports, feature requests, missing data, or praise.' It also advises what not to include ('do not include the end-user's prompt verbatim') and notes a rate limit. However, it does not explicitly exclude usage for other purposes compared to siblings, though the context implies this tool is only for feedback.

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

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

The description goes beyond annotations by detailing the tool's behavior: it walks child markets, extracts dates/thresholds, sorts them, and reports violations. It explains the monotonicity principle and output format. This adds value without contradicting annotations (readOnlyHint, openWorldHint, destructiveHint).

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 detailed paragraph. It is well-structured: purpose, rationale, usage, and output. While informative, it could be slightly more concise, but it earns its 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 single parameter, no output schema, and annotations, the description is complete. It covers input, logic, and output format effectively, leaving no ambiguity about what the tool does and how to use 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 has one parameter 'event' with a description. The description adds meaning by explaining the expected input (slug or URL) and how it's used. With 100% schema coverage, baseline is 3, but the description provides additional context, warranting a 4.

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

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

The description clearly states the tool's purpose: to find arbitrage opportunities within a Polymarket event by checking monotonicity violations. It specifies the verb 'find', the resource 'arbitrage opportunities', and the method, distinguishing it from siblings like polymarket_edges.

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

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

The description explains when to use the tool: when an event has multiple 'by [date]' or 'by [threshold]' markets. It tells how to use it: pass an event slug or URL. It does not explicitly list alternatives or when not to use, but the context is clear.

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

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

The description goes beyond annotations by detailing the process: scanning top markets, grouping by asset, fetching price history once, computing model probability, and ranking by edge magnitude. It also discloses that it is V1 and uses FRED + coinpaprika data. This provides rich behavioral context that complements the readOnlyHint and openWorldHint 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 four sentences long, which is slightly above average but still concise. It is front-loaded with the core purpose and progressively adds details. Every sentence contributes meaningful information, though it could be slightly more streamlined.

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 lack of an output schema, the description explains that the tool returns top N ranked by edge magnitude with suggested trade direction. It also covers the input parameters, the use case, and the underlying model. This provides a complete picture for an agent to understand what the tool does and what it returns.

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

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

With 100% schema coverage, the parameters already have clear descriptions. The tool description adds value by stating default values (limit=10, window='1wk', min_edge_pp=0.5) and explaining that the output includes suggested trade direction. This contextualizes the parameters 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 scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price. It specifies 'V1 covers crypto-price bets' and mentions the purpose: 'discover opportunities without paging through hundreds of markets by hand.' This distinguishes it from related tools like polymarket_arbitrage without explicit naming.

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 tool is built for the 'what should I bet on today' question and helps discover opportunities. However, it does not mention when not to use it or provide alternatives, such as pointing to polymarket_arbitrage for arbitrage scenarios.

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

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

No annotations provided, so description carries full burden. It discloses that omitting key lists all memories, and that memories persist across sessions. Could mention that recall is read-only and non-destructive, but not required.

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 wasted words. Front-loaded with verb and resource, then usage guidance. Every sentence earns its place.

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

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

For a simple tool with one optional parameter and no output schema, the description is complete. It explains both retrieval and listing behaviors, and notes persistence across sessions.

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

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

Schema coverage is 100% and description adds that omitting key lists all keys, which clarifies the parameter's behavior. Baseline 3 since schema already describes the key parameter adequately.

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

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

The description clearly states the tool retrieves a stored memory by key or lists all memories, with a specific verb 'Retrieve' and resource 'memory'. It distinguishes from siblings like '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 tells when to use (to retrieve context saved earlier) and how to use (omit key to list all). No alternative tools mentioned, but usage is clear and distinct from siblings.

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

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

With no annotations, description fully carries the burden. Discloses parallel fan-out, return structure (structured changes, total_changes count, URIs), input format, and entity type restriction. Missing details on error handling or missing entity, but overall transparent for a read-only 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?

Description is well-structured: core purpose first, then fan-out explanation, then format details, then return type, then use cases. Each sentence adds information, but could be slightly more terse without losing clarity.

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

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

No output schema, but description enumerates return fields (structured changes, count, URIs). Inputs are fully covered with examples. Minor gaps: no mention of rate limits or error conditions, but these are not critical for this type of tool. Overall sufficiently 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 descriptions cover all 3 parameters (100% coverage); description adds meaningful context: explains behavior for 'type' (only company), offers typical relative values for 'since' (e.g., '30d'), and clarifies 'value' can be ticker or CIK. This adds significant value beyond the schema.

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

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

Description clearly states the core purpose: 'What's new about an entity since a given point in time' and elaborates with specific fan-out to SEC EDGAR, GDELT, USPTO, making it distinct from sibling tools like entity_profile or compare_entities.

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 recommends usage for 'brief me on what happened with X' or change-monitoring workflows. Provides examples of acceptable 'since' formats. Could be improved by mentioning when not to use, but current guidance is clear enough given sibling 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?

Discloses that memory persistence depends on authentication (persistent for authenticated users, 24h for anonymous). With no annotations, description carries full burden and addresses safety concerns well. Could mention if overwriting same key is allowed.

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: purpose, usage examples, behavioral note. No redundant information. Front-loaded with 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?

Given simple tool with 2 required string params and no output schema, description covers purpose, usage, and persistence behavior. Could be complete for typical use. Slight gap: no mention of whether values are truncated or if keys are case-sensitive.

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

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

Schema coverage is 100%, and schema already describes parameters with examples. Description adds usage context but no new parameter 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?

Clearly states the verb 'store' and resource 'key-value pair in session memory'. Distinguishes itself from siblings like 'forget' and 'recall' which are complementary operations.

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?

Describes when to use ('save intermediate findings, user preferences, or context across tool calls') and mentions persistence differences based on authentication. Could explicitly exclude when not to use (e.g., for ephemeral data).

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?

No annotations provided, so description must disclose behavior. It mentions accepted inputs, return fields, and version support, but lacks details on error handling, authentication, or rate limits.

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

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

The description is two sentences, front-loading the purpose and value proposition. No redundant information; every sentence adds necessary detail.

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), the description covers purpose, inputs, and outputs well. It lacks edge-case behavior but is adequate for the complexity.

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

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

Schema coverage is 100%, so parameters are defined. The description adds examples (AAPL, 0000320193) and clarifies acceptable formats (ticker, CIK, name), 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 verb 'resolve' and the resource 'entity to canonical IDs'. It provides specific input types (ticker, CIK, name) and examples, distinguishing it from sibling tools like 'ask_pipeworx' by noting it replaces multiple lookup calls.

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

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

The description implies when to use (single call vs. 2-3 lookups) and specifies version v1 for company type. It does not explicitly state when not to use, but the context is clear.

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

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

With no annotations, the description carries full burden and discloses the bias (recommendation weighted toward no) and evaluation dimensions (passive aggression, regret probability). This is transparent, though additional behavioral details could be added.

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?

A single, information-dense sentence with no wasted words. It front-loads the purpose and includes a key behavioral trait.

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 analysis tool with an output schema, the description is sufficient. It covers purpose and bias; no missing critical context.

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

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

Schema coverage is 100% with clear descriptions and examples. The description adds only the recommendation bias, not extra parameter meaning, so baseline 3 is appropriate.

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

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

The description clearly states the tool analyzes whether to send an email, evaluating passive aggression and regret probability, with a recommendation. The verb 'analyze' and resource 'email' are specific, and the tool is distinct from siblings.

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

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

The description implies use when uncertain about an email, but does not specify when not to use or mention alternatives. The 'heavily weighted toward no' provides context but no 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?

No annotations are present, so the description carries the full burden. It discloses the return format (verdict, structured form, actual value with citation), mentions version limitations (v1 supports only company-financial claims), and indicates it is a read-only operation. Lacks specifics on error handling or rate limits, but adequate.

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 with no redundant words. Front-loaded with the core purpose, followed by scope, outputs, and a note on efficiency. 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 a single parameter and no output schema, the description covers the tool's purpose, domain, return values, and use case. It does not mention potential error conditions or prerequisites (e.g., need for market data), but these are reasonable omissions for a well-defined 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?

The single parameter 'claim' has a schema description that specifies natural-language factual claims with examples. The tool description adds context about the types of claims supported (financial) and example phrasing, which enriches 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?

Description clearly states the tool fact-checks natural-language claims against authoritative sources, specifies it handles company-financial claims via SEC EDGAR+XBRL, and lists concrete outputs (verdict, citation, delta). This differentiates it from siblings like ask_pipeworx or compare_entities.

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

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

Description implies usage for verifying financial claims and highlights efficiency gains over sequential agent calls, but does not explicitly state when not to use it or contrast with sibling tools. More explicit guidance could improve score.

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