Mastodon

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

Description goes beyond annotations by disclosing that the default model is free (Workers AI), that Anthropic calls require a user-provided API key (BYO key with direct billing), and specifies the return structure (per-model {score, confidence, signals, raw_response} + combined view). No contradictions with annotations.

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

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

Description is four sentences, front-loaded with the core action and scope. Each sentence adds unique information (default model, BYO key, return format, use cases) without redundancy. Exceptionally concise for the amount of detail provided.

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

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

Despite no output schema, the description adequately explains the return format and key behavioral details (free vs paid, idempotence implied by readOnly hints). The 4 parameters are fully documented in schema and description covers usage scenarios, making it complete for an agent to invoke correctly.

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

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

Schema coverage is 100% with clear parameter descriptions, and the tool description adds valuable context: entity examples, supported models with free default, API key format requirement, and context disambiguation purpose. This significantly enhances understanding beyond the schema alone.

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

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

Description clearly states the tool probes LLMs for knowledge about an entity and scores visibility (0-100) per model. It specifies the default model and optional Anthropic integration, and lists concrete use cases (AI-marketing audits, pre-launch checks, competitive monitoring), distinguishing it from siblings 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?

Description provides clear context for when to use the tool (e.g., for brand visibility checks) and explains the conditions for using the _apiKey parameter. However, it does not explicitly state when NOT to use the tool or mention alternatives, though siblings offer implicit differentiation.

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, but description fully discloses behavior: it picks the right tool, fills arguments, returns result. Implies it handles routing and argument inference, which is transparent.

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

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

Concise, front-loaded with purpose, followed by examples. Every sentence adds value. Could be slightly shorter but no fluff.

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

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

Given simple schema (1 param, no output schema), the description is complete enough. Explains what happens internally (tool selection, argument filling) and provides examples. No output schema 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% with a single parameter 'question' described as 'Your question or request in natural language'. The description adds examples of valid questions, but the schema already defines the 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 answers natural language questions by selecting the best data source. It distinguishes itself from siblings by offering a unified interface over specialized tools like search or get_account.

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

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

Provides clear guidance: just describe what you need in plain English, no need to browse tools. Includes concrete examples. However, does not explicitly state when not to use it or mention alternatives.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds rich behavioral details: resolves market, classifies bet type, fans out to relevant packs (e.g., crypto+fred+gdelt), and returns a comparison. 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?

A single paragraph that front-loads purpose and efficiently covers input, process, and output. Every sentence provides meaningful information; no redundancy or filler.

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

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

Despite no output schema, the description explains the output structure (evidence packet + comparison). It covers input formats, the resolution and classification process, and the value proposition. For a core demo tool, this is sufficiently complete.

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

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

Schema coverage is 100%, but description adds value beyond schema by clarifying the three accepted formats for 'market' and explaining the 'depth' options with defaults. It doesn't merely repeat schema descriptions.

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

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

The description starts with a clear verb+resource: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies input formats (slug, URL, question text) and output (evidence packet plus comparison), and distinguishes from sibling tools by positioning itself as the core demo product that converts better.

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

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

Provides explicit use cases: 'should I bet on X?', 'what does the data say...', 'is there edge?'. While it doesn't explicitly list when not to use it or name alternatives, the context makes it clear this is the specialized bet research tool among general tools like search and ask_pipeworx.

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 fully discloses the returned data fields for each type (e.g., revenue, net income for companies; adverse-event report count for drugs) and mentions 'paired data + pipeworx:// resource URIs'. It does not mention destructive actions or rate limits, but the tool appears to be read-only and does not contradict known behaviors.

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 with no wasted words. The first sentence immediately states the tool's purpose and scope, followed by concise details for each type. The final sentence highlights the efficiency gain, making every sentence earn 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?

The description covers the main purpose, parameter semantics, and return data sufficiently for a comparison tool. It could mention whether the output is a single object or list, but the mention of 'paired data' and resource URIs provides adequate context. No output schema is provided, so the description serves its purpose well.

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 the description adds significant value beyond the schema by explaining the mapping of type to specific financial or regulatory fields and detailing the expected value format (tickers/CIKs for company, names for drug). This enriches agent understanding.

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

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

The description clearly states the verb 'Compare', the resource 'entities', and specifies the two entity types with their respective data fields. It distinguishes itself from sibling tools like 'ask_pipeworx' or 'search' by focusing on side-by-side 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 states when to use the tool (to compare 2-5 entities) and highlights efficiency benefits ('Replaces 8–15 sequential agent calls'). It does not provide explicit exclusions or alternative tools, but given no sibling comparison tools exist, this is acceptable.

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 the burden. It states it returns the most relevant tools with names and descriptions, which is clear. However, it doesn't mention any behavioral traits like whether it supports pagination, rate limits, or if it modifies state. With zero annotations, a higher score would require more behavioral detail.

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, all essential information. Could be slightly more structured, but it's efficient and front-loaded with purpose.

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

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

Given the simple tool (search query, return list), the description covers purpose and usage. No output schema, but the description states what is returned (tool names and descriptions), which is sufficient. Slight deduction for not mentioning limit parameter behavior beyond default/max.

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

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

Schema coverage is 100% so baseline is 3. Description does not add extra meaning beyond the schema's parameter descriptions. The query parameter's description already includes examples.

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 searches a tool catalog by describing what you need, returning relevant tools with names and descriptions. It distinguishes itself by specifying it should be called first when many tools are available.

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: 'Call this FIRST when you have 500+ tools available and need to find the right ones.' No ambiguity about its role.

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

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

Discloses that it returns pipeworx:// citation URIs and replaces many sequential calls. Implicitly a read operation, but no annotations are provided. Could be more explicit about side effects, 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?

Four sentences with clear purpose, details, guidance, and exclusion. No fluff, 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?

Lacks output schema but mentions return type (citation URIs). Could detail structure more, but sufficient for agent to understand.

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. Adds extra context: explains type enum only 'company', gives examples for value, and warns names not supported.

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

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

The description clearly states it returns a full profile of an entity across multiple packs, specifically for company type. It lists data sources and distinguishes from sibling tools by noting federal contracts use a different tool.

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

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

Explicitly describes when to use (for full company profiles) and when not to use (federal contracts). Also advises to use resolve_entity first if only a name is available.

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 deletion but does not disclose consequences (e.g., irreversibility, authorization requirements, or whether an error occurs if key missing).

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

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

The description is a single, front-loaded sentence that efficiently conveys the core purpose with 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 (1 param, no output schema, no annotations), the description is minimal. It lacks information about return values, error handling, or prerequisites, leaving gaps for an AI agent.

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

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

Schema coverage is 100% and the schema already describes 'key' as 'Memory key to delete'. The description repeats 'by key' but adds no new 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?

The description uses a specific verb ('Delete') and resource ('stored memory'), and identifies the tool by key. It is concise and clearly distinguishes from siblings like 'remember' (store) and 'recall' (retrieve).

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 the tool is for deleting memories by key, but provides no guidance on when to use it versus alternatives, no prerequisites (e.g., key existence), and 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, indicating a safe, non-destructive operation. The description adds context on how it works (fetches page, extracts title/description/key links, emits markdown format), enhancing transparency without contradicting annotations.

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

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

The description is a single paragraph, front-loaded with the purpose and key features. Every sentence adds value, with no fluff. It is efficient 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?

The tool has low complexity (2 params, no output schema). The description explains the output format and behavior sufficiently for the use cases. It lacks details on error handling or edge cases, but given the simplicity, it is reasonably complete.

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

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

Schema coverage is 100%, so the schema already documents both parameters well. The description does not add significant meaning beyond the schema; it restates the url parameter as 'Full URL' and max_links as 'Maximum number of link entries' with default/max values. This is adequate but not additive.

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 generates an llms.txt file for a URL, specifying the verb 'Generate', the resource 'llms.txt file', and the target. It also names AI crawlers (ChatGPT, Claude, Perplexity) and mentions the output format, making the purpose very specific and distinguishable 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 explicitly lists three use cases: indexing a client's site, drafting for own projects, or auditing competitors. It does not mention when not to use or alternative tools, but the provided context is clear and sufficient for most 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?

The description indicates the tool is a read-only operation ('Get'), which is consistent with the lack of annotations. It adds behavioral context by specifying 'public' and requiring a 'numeric account ID', which are important constraints not evident from the schema alone.

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-formed sentence that conveys all necessary information without any extraneous words. It is front-loaded with the core 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's simplicity (one parameter, no output schema, no annotations), the description is nearly complete. It explains what the tool does and what input is required, leaving no major gaps for the agent.

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

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

The input schema already has 100% coverage with a clear description for the 'id' parameter. The tool description adds no further parameter semantics beyond what the schema provides, so a baseline score of 3 is appropriate.

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

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

The description clearly specifies the verb 'Get', the resource 'public Mastodon account profile', and the method of identification 'by numeric account ID'. This distinguishes it from sibling tools like 'get_timeline' and 'search'.

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

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

The description implies that the tool is for retrieving public profiles, which helps an agent decide when to use it versus other tools. However, it does not explicitly state when not to use it or mention alternatives.

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

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

No annotations are present, so the description must disclose behavior. It indicates the tool retrieves recent posts but does not specify whether it is read-only, if authentication is needed, or any rate limits. The mention of 'mastodon.social' provides some context about the source.

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 concise sentence with no wasted words. It front-loads the action and resource effectively.

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 one parameter and no output schema, the description is adequate but lacks details like post format or whether results are paginated. Given the low complexity, it is minimally complete.

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

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

Schema coverage is 100%, so the description need not repeat parameter details. However, the description adds no extra meaning beyond the schema; it does not explain how 'limit' affects pagination or ordering.

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 ('get'), the resource ('posts'), and the source ('mastodon.social public timeline'), making the tool's purpose unambiguous. It distinguishes itself from sibling tools like 'get_account' and 'get_trending' by specifying the public timeline 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?

No guidance is provided on when to use this tool versus alternatives like 'search' or 'get_trending'. There is no mention of context for using the public timeline or any 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does not state whether the tool is read-only, requires authentication, or any other behavioral traits.

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, clear sentence with no unnecessary words. It is appropriately concise.

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

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

Given the tool's simplicity (one optional parameter, no output schema), the description is minimally adequate but lacks important context about the data format or any restrictions.

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

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

Schema coverage is 100%, so the baseline is 3. The description does not add any additional meaning to the 'limit' parameter beyond what the schema already provides.

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

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

The description states the action ('Get'), the resource ('trending statuses'), and the instance ('mastodon.social'). It clearly indicates what the tool does, though it could differentiate it from siblings like 'get_timeline' more explicitly.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'get_timeline' or 'search'. It does not mention any prerequisites or 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?

No annotations are provided, so the description alone must cover behavioral traits. It discloses rate limiting (5 per day) and the 'free' tag, and instructs not to include verbatim prompts. However, it does not mention what happens after sending (e.g., confirmation, response time) or any permissions 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?

The description is four concise sentences: purpose, use cases, instruction, rate limit. Front-loaded with the action and immediately useful criteria. No redundant or extraneous wording.

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 (sending feedback) and no output schema, the description covers purpose, usage, rate limit, and privacy. However, it does not mention any confirmation or response after submission, which might be useful context for an AI agent.

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

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

Schema coverage is 100% with descriptions for all parameters. The description adds value beyond schema by providing usage examples ('describe what you tried') and constraints ('2000 chars max'). It also reinforces the enum values implicitly.

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

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

The description states '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 search, recall, or get_account, which have 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 says when to use this tool (for bug reports, features, data gaps, praise) and what not to do (include end-user prompt verbatim). It also mentions rate limiting (5 per day), providing clear usage boundaries.

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

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

Annotations already cover read-only, idempotent, open-world. Description adds caching details (5min-1h), derivation from CF analytics, and data composition (no PII), which goes beyond annotations.

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

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

Concise 4-sentence description, front-loaded with purpose, followed by use cases and technical details. No unnecessary words.

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

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

Despite no output schema, description clearly states return structure (top tools, top packs, call volume) and mentions caching. For a read-only aggregation tool, this is 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?

Single parameter with 100% schema coverage including enum and description. Description adds contextual meaning about short vs long windows, but schema already defines the options clearly.

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 explicitly states it returns top tools, top packs, and call volume over a window, with clear distinction from siblings like 'get_trending' by specifying AI agent perspective.

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

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

Provides three specific use cases (discovery, confirmation, alignment) but lacks explicit when-not-to-use guidance, though the enumerated uses effectively cover common scenarios.

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

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

Annotations already indicate read-only, non-destructive, and open-world behavior. The description adds valuable context: the tool checks monotonicity violations, walks child markets, searches across events, and returns ranked opportunities with trade direction and reasoning. No contradictions with annotations; the description fully discloses the tool's operational logic and output.

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

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

The description is concise yet comprehensive, front-loaded with the primary purpose in the first sentence. It then structures the two modes clearly, each with explanation and examples. Every sentence serves a purpose, with no irrelevant details. The length is appropriate for the tool's complexity.

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 input schema covers all parameters and annotations are present, the description provides complete context: explains the algorithm, the two modes, and the output format ('Returns ranked opportunities with suggested trade direction + reasoning'). There is no output schema, but the description adequately describes what the tool returns. No gaps remain for an agent to understand how to invoke the tool correctly.

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

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

Input schema has 100% coverage with good parameter descriptions. The tool description reinforces and elaborates on these, explaining the two modes and why cross-event mode is needed. It adds meaning beyond the schema by providing context on when to use each parameter, though the schema descriptions already capture the basics.

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

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

The description clearly states the tool's purpose: 'Find arbitrage opportunities on Polymarket by checking for monotonicity violations across related markets.' It specifies two distinct modes (event and topic), providing a specific verb and resource, and differentiates between them internally. Although it doesn't mention sibling tools, the purpose is unambiguous and well-defined.

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

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

The description explicitly explains when to use each mode: use 'event' for a single Polymarket event slug, and 'topic' for cross-event searches. It even provides a concrete example of when cross-event mode is necessary ('Polymarket lists each cutoff as its own event'). However, it does not compare this tool to sibling alternatives like 'polymarket_edges', so it lacks guidance on when to use this tool vs. other tools.

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

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

Annotations already mark it as read-only and non-destructive. The description adds useful context: the model (lognormal from FRED + coinpaprika), the workflow (groups by asset, fetches price history once), and ranking by edge. 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 front-loaded with purpose and structured logically, but it is slightly verbose with a few redundant phrases (e.g., 'V1 covers crypto-price bets'). Still, every sentence earns its place for clarity.

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

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

Given high schema coverage, clear annotations, and no output schema, the description fully explains what the tool does, how it works, and what it returns. It provides sufficient context for an AI agent to decide when 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% with all parameters described, so baseline is 3. The description does not add new semantics beyond what the schema provides, only reiterates defaults that are already in the schema.

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

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

The description clearly states the tool scans high-volume Polymarket markets, finds disagreements with Pipeworx data, and returns top edges with trade direction. This distinguishes it from siblings like ask_pipeworx, bet_research, and polymarket_arbitrage.

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

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

It explicitly frames the use case as 'what should I bet on today' and mentions it saves agents from manually paging through markets. However, it does not specify when not to use this tool or mention alternatives.

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

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

Annotations already indicate readOnly, openWorld, idempotent, not destructive. Description adds that it returns leg-by-leg prices and spread in raw probability and percentage points, explaining the behavioral trait that it computes the delta.

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 moderately long but well-structured with clear sections for modes and returns. Every sentence adds value, but could be slightly more concise.

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

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

Despite no output schema, description adequately explains return values. Covers both topic and explicit modes, and explains the underlying arb signal. Comprehensive for a spread calculation 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 100%, baseline 3. Description enhances by explaining topic pre-mapped shortcuts and explicit parameter override logic, adding meaning beyond the schema's 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?

Clearly states the tool computes cross-venue spread between Kalshi and Polymarket for the same resolving question. Distinguishes itself from siblings like polymarket_arbitrage by focusing on the spread and explicit modes.

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?

Explains two modes (topic shortcuts and explicit tickers) with clear examples. Provides enough context for when to use each, though does not explicitly state when not to use.

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

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

No annotations provided, so description carries full burden. It states it retrieves a memory or lists keys, which is straightforward. Lacks details on behavior if key doesn't exist or on the format of returned data.

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 concise sentences, front-loaded with the core action, no unnecessary words.

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

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

Given no output schema and no annotations, the description adequately explains the tool's functionality and usage context. Could mention return format or error behavior, but for a simple memory retrieval tool, it's sufficient.

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

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

Schema coverage is 100%, so the schema already describes the parameter. The description adds the nuance that omitting the key lists all memories, which is helpful but not extensive.

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 a memory by key or lists all memories when key is omitted. The verb 'retrieve' and resource 'memory' are specific, and 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?

Explains when to omit key (to list all) and provides context about retrieving previously saved information. Does not explicitly mention when not to use or alternatives, but the guidance 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 fully discloses behavior: for type='company' it fans out to three sources in parallel. Parameter formats ('since' accepts ISO or relative) and return structure (structured changes, count, URIs) are mentioned. Missing details like error handling or auth but sufficient 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?

The description is concise at 5 sentences, front-loaded with purpose, structured logically: purpose, behavior, parameter details, return format, use cases. Every sentence adds value with 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?

Covers entity types, parameter constraints, fan-out behavior, return values, and use cases. No output schema exists, so the description compensates by describing return structure. Minor gaps: no mention of error scenarios or authentication, but overall complete for a moderate-complexity 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%, but the description adds significant value: it explains the fan-out for type='company', provides examples for 'since' (ISO, relative, typical values), and clarifies 'value' accepts ticker or CIK. This goes beyond the schema's basic descriptions.

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

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

The description clearly states the tool's purpose: 'What's new about an entity since a given point in time.' It specifies a specific verb (get recent changes) and resource (entity with type, value, since). This clearly distinguishes it from sibling tools like entity_profile or get_timeline.

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 recommends usage: 'Use for "brief me on what happened with X" or change-monitoring workflows.' While it does not explicitly state when NOT to use or name alternatives, the context is clear enough for appropriate selection.

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 discloses key behavioral traits: persistence differs by authentication, and anonymous sessions last 24 hours. This adds significant value beyond the input schema. However, it does not mention what happens if a key already exists (overwrite or error).

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: first states the core function, second gives usage examples, third clarifies persistence. It is concise and front-loaded, though the third sentence could be seen as behavioral transparency 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 params, no output schema), the description covers the main points: what it does, when to use it, and persistence behavior. It does not explain return values, but no output schema exists. Slight gap on overwrite behavior, 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?

Schema description coverage is 100%, so baseline is 3. The description does not add further meaning to parameters beyond what the schema provides; it only reiterates the storage purpose. The schema already describes key and value clearly.

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 stores a key-value pair in session memory, specifying the verb 'store', the resource 'key-value pair', and the scope 'session memory'. It also differentiates from siblings like 'forget' and 'recall' by indicating it saves data for later use.

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

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

The description provides explicit guidance on when to use the tool: 'save intermediate findings, user preferences, or context across tool calls'. It distinguishes usage based on authentication status (persistent vs. 24-hour memory), but does not explicitly mention when not to use it or mention alternatives.

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

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

With no annotations, the description discloses the return format (ticker, CIK, name, pipeworx:// URIs) and efficiency benefit. It does not mention auth or rate limits, but for a read-like operation, this 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?

Two sentences, front-loaded with the core purpose. The second sentence adds specific version details without redundancy. Every word 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?

Given no output schema, the description explains return values. It covers both parameters, version constraints, and usage context. No missing information given the tool's simplicity.

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 description adds examples (AAPL, 0000320193, Apple) and explains that value accepts multiple formats. It clarifies the type enum is currently limited to company, supplementing 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 resolves an entity to canonical IDs across Pipeworx data sources. It specifies the entity type (company) and accepted input formats (ticker, CIK, name), distinguishing it from alternatives by noting it replaces 2–3 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 the tool (single call vs. multiple) and provides version-specific details (v1 supports company only). However, it does not explicitly exclude other use cases or compare with sibling tools like search_calls.

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?

Describes the probe mechanism, ranking, and returned metrics (score, confidence, signal density). Annotations (readOnly, openWorld, idempotent, not destructive) are consistent and the description adds meaningful behavioral context beyond them.

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

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

Concise, front-loaded with the core action. Every sentence serves a purpose – describing function, use case, and output. 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 purpose, process, and output (ranked list with metrics) despite lacking an output schema. With 4 params fully documented, the description is complete and actionable.

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?

All parameters have schema descriptions (100% coverage). The description adds valuable context: first entity treated as 'subject', rest as competitors, and clarifies the models parameter usage. Exceeds 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?

Clearly states it compares AI visibility across entities side-by-side, using probes, ranking, and surfacing most/least recognized. Distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (generic comparison).

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

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

Explicitly frames usage as competitive AI-marketing audits with a concrete example question. Does not explicitly state when not to use or provide alternatives, but the context is clear enough.

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 discloses the search targets (accounts, statuses, hashtags) but does not describe behavioral traits like rate limits, pagination, or authentication needs. The description is adequate but not comprehensive.

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

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

The description is a single sentence of moderate length, front-loading the purpose. It is concise but could be slightly improved by front-loading the key actions.

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 moderate complexity (3 parameters, no output schema), the description covers the basic purpose but lacks details on return format or behavior for different types. Completeness is adequate but not thorough.

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 does not add any meaning beyond what the schema already provides for parameters like type and limit. It correctly states the search capability but no extra context.

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

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

The description states the tool searches Mastodon for accounts, statuses, or hashtags, which is a specific verb and resource. It distinguishes from siblings like get_account (which retrieves a single account) and get_timeline (which fetches a timeline), but could be clearer about the exact 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 mentions searching on mastodon.social, but does not specify when to use this tool versus alternatives like get_account or get_timeline. No explicit when-not-to-use guidance is provided.

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

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

No annotations are provided, so the description carries the full burden. It discloses the tool's behavior: it looks up authoritative sources (SEC EDGAR + XBRL), returns verdicts, extracted data, actual values with citations, and percent delta. It does not mention any destructive actions or side effects; the description implies read-only behavior. This is sufficient for an agent to understand what happens.

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

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

The description is extremely concise: two sentences with no redundant information. The first sentence states the core function, and the second provides supporting details and differentiation. It is well-structured and 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?

Given the tool has a single simple parameter and no output schema, the description fully explains the purpose, supported claims, source, and output format. It is complete for an agent to understand when 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?

The input schema covers 100% of parameters with descriptions. The schema already explains the 'claim' parameter as a natural-language factual claim with examples. The description adds no new semantic information beyond what is in the schema, so baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: fact-check natural-language claims against authoritative sources for company-financial data. It specifies the supported domain (public US companies, revenue/net income/cash) and the output types (verdict, extracted form, actual value with citation, percent delta). It also distinguishes itself by noting it replaces 4-6 sequential agent 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 explicitly states what types of claims are supported (company-financial, public US companies) and implies other claims are not. It also indicates when to use it as a consolidated alternative to a multi-step process. However, it could be improved by explicitly stating when not to use it or suggesting alternatives for non-financial claims.

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