Jsonplaceholder

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 discloses that the tool picks the right tool and fills arguments, but does not mention any side effects, error handling, or response format. The behavior is moderately transparent but lacks details on what 'returns the result' entails.

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 (two sentences plus examples) and front-loaded with the core action. Every sentence adds value: the first states purpose, the second explains how it works, and examples illustrate usage. No wasted words.

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

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

Given the tool's simplicity (one parameter, no output schema, no nested objects), the description is mostly complete. It explains the input format and behavior, but lacks details on output structure or error cases. However, for this type of tool, the description provides sufficient context for an agent to select and 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% for the single parameter 'question'. The description adds value by explaining that the parameter can be any natural language request and provides examples, which goes beyond the schema's minimal description. The simplicity of the parameter means the description adequately compensates.

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: it answers questions in plain English by selecting the best data source. It uses specific verbs ('ask', 'get answer') and a clear resource ('best available data source'). The distinction from sibling tools is implicit but sufficient given its unique role.

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 strong usage guidance by stating the tool handles natural language queries without needing to browse tools or learn schemas. It gives examples of appropriate queries. However, it does not explicitly state when not to use this tool or mention alternatives like using specific data tools directly.

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

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

Annotations declare readOnlyHint=true, openWorldHint=true, destructiveHint=false, and the description adds contextual behavior: the tool fans out to appropriate packs based on classification (e.g., crypto+fred+gdelt for BTC bet) and returns a market-vs-model comparison. This goes beyond the structured annotations, though it could mention data freshness or error handling.

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 core action. It is slightly long but every sentence contributes meaning (purpose, inputs, process, return value, usage context). No redundancy, but could be trimmed slightly for brevity.

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 (multiple data sources, classification, return of evidence packet and comparison) and lack of output schema, the description adequately explains what the tool does and returns. It mentions the demo product context. It could be more complete by specifying the format of the evidence packet or potential limitations.

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

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

Schema description coverage is 100% with both parameters described. The description adds value by clarifying the 'market' parameter can be a slug, URL, or question text, and notes the default for 'depth' is 'thorough'. This exceeds the schema, providing practical usage hints.

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

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

The description clearly states the tool's purpose: research a Polymarket bet by pulling relevant Pipeworx data. It specifies the verb 'research', the resource 'bet', and elaborates on the process: resolve market, classify bet, fan out to packs, return evidence packet and comparison. This distinguishes it from sibling tools like ask_pipeworx, which is more general.

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

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

The description provides explicit examples of when to use the tool: 'should I bet on X?', 'what does the data say about this Polymarket market?', or 'is there edge in this bet?'. It also explains that this tool is preferred over having agents discover packs themselves. However, it lacks explicit when-not-to-use guidance or alternatives for non-bet research 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?

No annotations, so description must carry burden. It describes return data (paired data, URIs) but omits potential errors, rate limits, or behavior on missing entities.

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, each valuable: first states purpose, second details data, third highlights efficiency. 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?

Covers key aspects: types, data fields, resource URIs, and performance benefit. Lacks output schema, but description compensates. Could mention error responses.

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 already has 100% coverage; description adds examples and clarifications for each parameter, enhancing understanding 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 specifies comparing 2-5 entities by type, with distinct data fields for company and drug, and distinguishes from sibling tools which are individual entity 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?

Implies efficiency and replacement of sequential calls, but lacks explicit when-not-to-use or alternatives. Not misleading but could be clearer.

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 carries full burden. It explains the tool searches and returns tools, but does not disclose any behavioral traits like rate limits, authorization needs, or side effects. Since it is a search tool, the lack of side effects is somewhat implicit, but more transparency would improve the score.

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

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

The description is three sentences long, each adding value: purpose, return value, and usage guidance. No filler or redundancy. 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?

Given the tool's simplicity (2 params, no output schema), the description is complete enough. It explains what the tool does, when to use it, and what to expect. The only minor gap is not stating the return format explicitly, but 'returns the most relevant tools with names and descriptions' suffices.

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

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

Schema coverage is 100%, so the schema already documents both parameters. The description adds context by explaining the purpose of the query ('Natural language description of what you want to do') with examples, and clarifies the limit's default and max values, which adds meaning beyond the schema's 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 searches a tool catalog and returns relevant tools, with a specific verb 'search' and resource 'Pipeworx tool catalog'. It distinguishes itself from siblings by advising to call this FIRST when many tools are available, which no sibling does.

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 this tool ('when you have 500+ tools available and need to find the right ones') and provides a call-to-action ('Call this FIRST'). This gives clear guidance on when to use it 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?

With no annotations, the description carries full burden. It discloses return of citation URIs and inclusion of multiple data sources. It implies read-only behavior and does not contradict any annotations. However, it lacks mention of rate limits or authentication, but this is minor for a read 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, well-structured paragraph of about 60 words. It is front-loaded with the core purpose, then details what is returned and when to use alternatives. Every sentence adds value, and there is no redundancy.

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

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

Given the lack of output schema, the description sufficiently explains what the tool returns (specific data types and citation URIs) and its advantage (replaces many calls). It could mention response structure or prerequisites, but overall it provides enough context for an agent to decide 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 coverage is 100%, so baseline is 3. The description adds some context about alternatives for names but does not significantly extend the parameter meanings beyond the schema's descriptions. It reinforces the allowed values and provides usage guidance but not deeper semantics.

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 returns a 'full profile' across multiple data sources, listing specific types (SEC, XBRL, patents, news, LEI). It distinguishes itself from the sibling 'usa_recipient_profile' and hints at 'resolve_entity' for name resolution, providing clear differentiation.

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

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

The description provides explicit context: use for broad entity profiles, but for federal contracts call 'usa_recipient_profile' directly. It also implies when not to use it (if only needing one data type), though it doesn't compare against other siblings like 'compare_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?

No annotations are provided, so the description must carry the full burden. It states 'Delete' but does not mention if the operation is irreversible, what happens to related data, or any permission requirements. For a destructive operation, more behavioral context is needed.

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 short sentence with no wasted words. It is front-loaded with the action 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?

Given the tool performs a deletion with no output schema, the description should clarify side effects, irreversibility, or success indicators. It lacks completeness for a destructive operation.

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

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

The schema already describes the 'key' parameter with 100% coverage. The description adds minimal value by stating 'by key', which is already clear from the schema. Baseline 3 is appropriate.

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

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

The description clearly states the action ('Delete') and the resource ('a stored memory') and identifies the key parameter. However, it does not distinguish from the sibling tool 'recall' or 'remember', which may also deal with memories.

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 'recall' (read) or 'remember' (write). The description lacks context for 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?

No annotations provided, so description carries full burden. Discloses returned fields (comment ID, name, email, body) but does not mention any side effects, idempotency, or limitations beyond the JSONPlaceholder context. Average for a read-only tool with no 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, front-loaded with action and resource, no unnecessary 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?

Tool is simple with one parameter, no output schema needed. Description covers purpose and return fields. No gaps identified given the low 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%, and description adds meaning by noting the range 1–100 for post_id. However, the description does not elaborate on the parameter beyond what schema already says, 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?

Clearly states verb 'Retrieve' and resource 'comments for a specific fake blog post'. Distinguishes from siblings like get_post/get_posts by specifying it returns comment 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 explicit when-to-use or when-not-to-use guidance. Context implies it's for retrieving comments by post ID, but does not mention alternatives or when other tools might be more appropriate.

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 burden of behavioral disclosure. It accurately describes a read operation (retrieve) but does not mention whether the tool has rate limits, data freshness, or error handling (e.g., what happens if ID is invalid). However, the tool is simple enough that the description is 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?

The description is two sentences with zero waste, front-loading the action and resource. Every sentence is essential.

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 (1 param, no output schema, no nested objects), the description is complete enough. It specifies the input and output fields, though it could mention the source (JSONPlaceholder) and possible constraints (like limited ID range).

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

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

Schema description coverage is 100%, so baseline is 3. The description adds context that IDs are 1–100, which matches the schema, but does not add 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 clearly states the tool retrieves a single post by ID and lists the returned fields (post ID, user ID, title, body text). It differentiates from siblings like get_posts (list) and get_comments (comments), but does not explicitly contrast with 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?

The description implies usage for retrieving one post by ID, but does not specify when to use this over get_posts (which likely returns all posts) or get_users. No alternative 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?

Discloses it's a read operation returning fake data, but no annotations exist. Description lacks detail on side effects, rate limits, or whether results are paginated.

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, each adding value: purpose, use case, content fields. Could be slightly more compact, but no wasted words.

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

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

For a simple read tool with one parameter and no output schema, description covers basics. Lacks mention of ordering, filtering, or error cases, but sufficient for basic prototyping.

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 for the single parameter 'limit', with description already explaining default and max. The tool description adds no additional parameter semantics 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?

Clearly states it retrieves fake blog posts from JSONPlaceholder for prototyping/testing, specifying exact data fields (ID, user ID, title, body). Distinguishes from sibling tools like get_post (singular) and get_comments.

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?

Implied usage for prototyping/testing, but no explicit guidance on when to choose this over get_post or get_comments, nor 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?

The description clearly states it returns fake data from JSONPlaceholder, which is critical for transparency. There are no annotations to contradict, and the behavioral insight that data is fake prevents misuse. No destructive or auth details needed 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?

Single sentence, no fluff. Every word earns its place. The key information (data source, fields) is front-loaded.

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

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

For a simple list tool with no parameters and no output schema, the description covers purpose, data source, and fields. Could mention that it returns all users without filtering, but otherwise 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?

No parameters in schema, but description explains what data is returned (fields). Schema coverage is 100% trivial. Description adds value by listing returned fields, which is helpful beyond the empty 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 retrieves fake users from JSONPlaceholder, listing the specific fields returned (name, username, etc.). This verb+resource combination is unambiguous and distinguishes it from siblings like get_comments or get_posts.

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 explicit when-to-use or alternative guidance. Since this is a basic list tool with no parameters, usage is straightforward, but the description does not discuss filtering or when to use get_users vs other list 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 provided, the description must carry the behavioral disclosure burden. It discloses the rate limit (5 per day) and the instruction to describe what was tried, but does not mention what happens after sending (e.g., confirmation, storage, or follow-up). For a simple feedback tool, this is largely 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?

The description is three sentences, front-loaded with the core purpose, and every sentence provides essential information (use cases, instructions, rate limit). No redundant or extraneous 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?

Given the tool's simplicity (no output schema), the description covers key aspects: purpose, usage, parameters, and constraints. It does not describe the response format, which is a minor gap, but for a feedback submission, acknowledgment is usually implicit. Overall, it provides sufficient context 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 value beyond the schema by providing usage guidance: it specifies character limit (2000 chars), advises 1-2 sentences, and reinforces the exclusion of verbatim prompts. This enhances the semantic clarity for the agent.

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 ('Send feedback') and specifies the audience ('Pipeworx team'). It lists explicit use cases (bug reports, feature requests, missing data, praise) and distinguishes this tool from siblings, as no other tool on the server serves the feedback function.

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 about when to use the tool (for various feedback types) and includes a negative instruction (do not include user prompt verbatim) and a rate limit. However, it does not explicitly exclude other scenarios or mention alternative tools, though none exist.

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, openWorldHint, and non-destructive. The description adds rich behavioral context: it walks child markets, extracts dates/thresholds from questions, sorts them, and reports violations. It explains the monotonicity rule and the output structure. No contradiction with annotations.

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

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

The description is moderately long but well-structured: it opens with the core purpose, explains the logic with an example, then gives usage instructions and output format. Every sentence adds value, though some phrasing could be tightened slightly.

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 only one parameter and no output schema, the description fully covers what the tool does, how to use it, how the results are structured (list with specific fields), and the underlying logic. No gaps remain for an agent to invoke it correctly.

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

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

Schema coverage is 100% with a single parameter 'event' described as slug or URL. The description adds value by explaining how the tool uses that input (walks child markets, extracts dates/thresholds). This helps the agent understand the expected format and purpose beyond the schema text.

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 by checking monotonicity violations. It gives a concrete example with Bitcoin price thresholds and explains the logic. It distinguishes this tool from siblings like 'bet_research' or 'compare_entities' by focusing on a specific arbitrage pattern within Polymarket events.

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 the agent to pass a Polymarket event slug or URL. It explains the condition for arbitrage (earlier market trading higher than later one). However, it does not explicitly state when not to use the tool or name alternatives from the sibling list for other types of analysis.

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 readOnly and non-destructive; description adds rich detail: uses lognormal model from FRED and coinpaprika, groups by asset, fetches price once, computes probability, ranks by edge. 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 succinct yet informative, front-loaded with the core purpose, followed by technical details, output, and use case. Every sentence adds value 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?

Given no output schema, the description explains returns (top N markets with edge magnitude and suggested direction). It covers all parameters and use case. Missing specific output field names, but sufficient for agent understanding.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description does not add new meaning beyond what the schema provides, meeting the baseline.

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 with largest edge. It specifies the verb 'scan' and resource 'Polymarket markets', and distinguishes from siblings like polymarket_arbitrage by focusing on opportunity 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?

The description explicitly states this tool is built for 'what should I bet on today' and helps discover opportunities without manual paging. It does not mention alternatives or when not to use it, 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 discloses that the tool can list all memories if key is omitted, and that it retrieves context saved earlier or in previous sessions. This adds value beyond the schema, but no annotations are provided, so the description carries the full burden. It does not mention any destructive behavior or 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?

The description is two sentences, front-loaded with the primary purpose, and every sentence adds value. It is concise and 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 simplicity of the tool (1 optional parameter, no output schema, no nested objects), the description is complete enough. It explains both modes of operation (retrieve by key or list all). No output schema means the agent must infer return format from context, but the description does not specify return structure, which is a minor gap.

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 context by explaining that omitting the key lists all memories, which complements the schema's description. However, it does not provide additional details about the parameter's format 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 clearly states the tool retrieves a stored memory by key or lists all memories when key is omitted, using specific verbs 'Retrieve' and 'list'. It distinguishes itself from sibling tools like 'remember' and 'forget' by focusing on retrieval.

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

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

The description explains when to use the tool ('to retrieve context you saved earlier') and implies when not to use it (when key is omitted, it lists all). However, it does not explicitly mention alternatives or when to avoid using it, such as when no memory exists.

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 carries the full burden. It discloses that the tool fans out to multiple sources in parallel, accepts relative dates, and returns structured changes with URIs. It does not mention any destructive effects or rate limits, but for a read‑oriented monitoring tool, the required transparency is met.

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 long, front-loaded with the key purpose, and every sentence adds essential information. There is no redundancy or filler. It packs a lot of useful guidance succinctly.

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 that there is no output schema, the description adequately explains the return structure (structured changes, total_changes count, pipeworx:// URIs) and the sources consulted. Sibling tools are all different, so no additional context is needed.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds value by explaining the fan‑out behavior, accepted date formats (ISO and relative), and the structure of the return value (changes + count + URIs). This goes beyond the schema's simple parameter 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 opens with a clear verb phrase 'What's new about an entity since a given point in time' and specifies the supported type (company) and the fan-out to multiple sources (SEC EDGAR, GDELT, USPTO). This immediately distinguishes it from sibling tools like 'compare_entities' or 'entity_profile', which serve different 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?

The description explicitly states use cases: 'Use for "brief me on what happened with X" or change-monitoring workflows.' It also mentions that only 'company' type is supported, guiding usage. It does not explicitly exclude other scenarios, but the context is sufficiently clear for an agent to decide 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?

No annotations provided, so description must carry the burden. It discloses persistence differences based on authentication. However, it doesn't specify if values can be overwritten, size limits, or if the tool has any destructive effects. Adequate but not exhaustive.

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 front-loaded action and examples. Concise and informative. Could be slightly more structured, but no wasted words.

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

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

Given the simplicity of the tool (2 params, no output schema), the description is complete. It covers purpose, usage, and persistence behavior. No missing critical information for an AI agent to use it 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 already has 100% coverage with good parameter descriptions. The description adds context about what kinds of values to store (findings, addresses, preferences, notes) but does not add meaning beyond the schema. Baseline 3 is appropriate.

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

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

Description clearly states the action (store), resource (key-value pair), and location (session memory). It distinguishes from sibling tools like 'recall' and 'forget' by explicitly mentioning storage.

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 provides context on persistence ('Authenticated users get persistent memory; anonymous sessions last 24 hours'). Could mention when not to use or alternatives, but purpose 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?

No annotations are provided, so the description carries full burden. It states the tool returns ticker, CIK, company name, and URIs. However, it does not disclose error handling, auth needs, rate limits, or case sensitivity. Some behavioral details are implied but not explicit.

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, front-loaded with the primary action, and every sentence adds value: purpose, version constraints, inputs, outputs, and efficiency. No wasted words.

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

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

Given low complexity (2 params, no output schema), the description fully covers inputs, outputs, and version info. It compensates for missing output schema by listing return fields (ticker, CIK, name, URIs). No gaps remain.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by providing examples (e.g., 'AAPL', '0000320193', 'Apple') and explaining the format, which goes beyond the schema's sparse descriptions. This elevates the score to 4.

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

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

The description clearly states it resolves an entity to canonical IDs, specifying that v1 supports type 'company' and accepts ticker, CIK, or name. It distinguishes itself from sibling tools, none of which perform entity resolution, making its purpose unique and specific.

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 mentions it replaces 2–3 lookup calls, implying efficiency. No explicit when-not or alternatives are given, but with no similar siblings, the usage context is clear. Lacking exclusions prevents a higher 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?

No annotations provided, so description carries full burden. It discloses the return values (verdict, structured form, actual value with citation, percent delta) and sources (SEC EDGAR + XBRL). There is no mention of destructive actions, authentication, or rate limits, but the read-only nature is implicit.

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

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

The description is concise with 4 sentences, each adding distinct value: domain scope, supported claims, return values, and efficiency benefit. 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 has one parameter, no output schema, and no annotations, the description adequately covers purpose, usage context, return format, and benefits. It leaves no critical gaps 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?

With only one parameter and 100% schema description coverage, the description adds meaning by specifying the type of claims (e.g., revenue/profit examples) beyond the schema's generic example. It clarifies the parameter's domain scope.

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 fact-checks natural-language claims against authoritative sources, specifying the domain (company-financial claims for public US companies) and listing verdict types. It also distinguishes itself by replacing 4-6 sequential agent calls, providing a clear verb+resource scope.

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

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

The description explicitly limits support to company-financial claims (revenue, net income, cash) for public US companies, giving clear context for when to use the tool. It also highlights efficiency gains over alternatives, but lacks explicit when-not-to-use guidance or alternatives for other claim types.

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