Space Feeds

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds value by noting the default free model, the need for an API key to probe Anthropic (with cost implications), and a summary of the return structure. No contradictions.

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

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

Three sentences, front-loaded with the core action and output, no redundant information. 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?

For a 4-parameter tool with no output schema, the description adequately explains what returns (per-model objects and combined view). It covers the main use cases and behavior, leaving no critical gaps given the annotations already present.

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 adds meaningful context: default model, that _apiKey is only needed for 'anthropic' in models, the 'context' parameter for disambiguation, and the format of the Anthropic API key (sk-ant-...). This elevates the score.

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 ('probe') and resource ('LLMs'), clearly stating the outcome ('score visibility 0-100 per model'). It distinguishes from sibling tools like 'ask_pipeworx' which are likely generic Q&A, making the purpose unambiguous.

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

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

The description lists concrete use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It does not explicitly state when not to use this tool or name alternatives among siblings, but the context provided is clear enough 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as false. The description adds that it routes queries to 4,552 tools across 1,160 sources, fills arguments automatically, and returns structured answers with stable citation URIs. This goes beyond annotations, but does not discuss potential limitations like rate limits or failure modes.

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 verbose, listing many domains and examples. While front-loaded with the preference directive, it could be more concise. Some repetition occurs (e.g., list of question types appears twice).

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 (routing to many sources) and no output schema, the description adequately explains what it returns (structured answer with citations) and provides concrete examples. It also addresses when to use companion tools, covering the main use cases.

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 describes 6 parameters (all aliases for 'question') with 100% coverage. The description adds that it accepts natural language and provides examples, but does not add significant new semantic information beyond what the schema already conveys.

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

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

The description clearly states that the tool answers factual questions using authoritative structured data with citations. It explicitly lists the domains it covers (SEC, FDA, FRED, etc.) and distinguishes itself from siblings like ask_pipeworx_grounded and deep_research, providing specific use cases for each.

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 guidance to prefer this tool over web search for factual queries. Lists numerous example question types and exact phrasings to use. Clearly states when to step up to alternatives (ask_pipeworx_grounded for hallucination-resistant answers, deep_research for broad/multi-part questions).

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=false. The description adds valuable behavioral context such as 'hallucination-resistant', the fact that it extracts answers using only tool results, and details potential refusal reasons ('not_in_source', 'no_tool_match', etc.). It also mentions the extra LLM call cost. 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 well-structured with front-loaded key information (the purpose and distinction). Each sentence adds value, including the return format, refusal reasons, and usage guidance. Despite being detailed, there is 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 complexity of the tool (routing across 4552 tools, 1160 sources, with explicit refusal handling), the description is complete. It explains the behavior, return structure, error conditions, and cost trade-off. The lack of an output schema is compensated by the detailed description of the return object and refusal reasons.

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 each alias (q, text, input, query, prompt, question). The description repeats that it accepts a natural language question and lists the aliases, but adds little beyond what the schema already provides. A baseline score of 3 is appropriate since no additional parameter meaning is introduced.

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 is a 'hallucination-resistant answer mode for high-stakes reads' and distinguishes it from 'ask_pipeworx' by outlining the exact process: routing, data fetching, and answer extraction using only tool results. It specifies return format with fields like answer, evidence, confidence, source, and refusal reasons, making the purpose very 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?

Explicitly tells when to use: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with concrete examples (financial verdicts, legal claims, medical lookups). Also gives an exclusion: 'prefer ask_pipeworx for casual lookups' and notes it costs one extra LLM call. This provides clear guidance on when to choose this tool over 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 a safe, read-only, idempotent tool. The description goes far beyond by detailing the research steps (resolve, classify, fan-out), response shapes, edge cases (low confidence, closed markets, wide spreads), resolution-rule risk, and fallback mechanisms. This provides deep behavioral transparency.

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 lengthy but well-structured with labeled sections (CLASSIFIERS, FAN-OUT EXAMPLES, etc.). It is front-loaded with the core purpose. While verbose, every sentence provides valuable detail for a complex tool. Minor room for trimming without losing information.

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

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

Given no output schema, the description thoroughly explains the response structure (result.market, result.analysis, result.evidence) and covers many edge cases (low confidence, closed markets, wide spreads, cancellation rules). It leaves little ambiguity about what the tool returns and how to interpret results.

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

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

Schema coverage is 100%, so baseline is 3. The description adds significant value by explaining the meanings of 'depth' options (quick vs thorough), the function of 'include_raw' (summarized vs full payloads), and the flexible format for 'market' (slug, URL, question). This enhances parameter understanding beyond the schema.

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

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

The description clearly states the tool researches Polymarket bets by pulling Pipeworx data in one call. It specifies inputs (slug, URL, question) and outputs (evidence packet + comparison). While it doesn't explicitly distinguish from sibling tools, the purpose is highly specific 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?

Explicit use cases are provided: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also includes guidance on inspecting result fields like market_match_confidence and cancellation_rule before acting. However, it does not explicitly compare with sibling tools like polymarket_arbitrage.

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 adds substantial behavioral context beyond the annotations (readOnlyHint, idempotentHint, etc.): it reveals that company data pulls latest 10-K financials with off-calendar fiscal year handling, drug data pulls adverse events, approval counts, and trial counts, and results are sorted by primary metric. 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 dense but well-structured, starting with query examples and then detailing behavior for each type. Though slightly verbose, every sentence adds necessary context for correct invocation.

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

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

Given the tool's complexity (two entity types, multiple data points, citation URIs), the description is remarkably complete. It covers what each type returns, how data is sorted, and includes citation references. Without an output schema, the description sufficiently explains the return format.

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

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

With 100% schema description coverage, the parameters are already well-documented. The description adds value by explaining the real-world meaning of values (e.g., tickers/CIKs for companies, drug names for drugs) and the distinction between types, justifying a score slightly above 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's purpose: side-by-side comparison of 2-5 companies or drugs. It provides numerous example user queries like 'Compare X and Y' and 'X vs Y', and explicitly distinguishes itself from sequential single-entity lookups, differentiating from siblings like entity_profile.

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 advises 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' giving a clear when-to-use directive. It also explains the data sources for each type (SEC EDGAR/XBRL for companies, FAERS/drug info for drugs) so the agent knows appropriate contexts.

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 false. The description adds significant context: decomposes questions, routes to 4,552 tools in parallel, returns findings packet with evidence/confidence/source/citation/gaps array, expected 15-60s, and that it never invents answers. 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?

Description is long but dense with value. Critical info (account required) is front-loaded. Every sentence adds purpose, guidance, or behavior. Minor redundancy could be trimmed but overall effective.

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 tool complexity, description is very complete: purpose, usage, behavior, parameters, output format (findings packet), limitations (gaps for breaking news), time expectation, and alternatives. No output schema, so explanation of return value is 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 baseline 3. Description adds meaning: 'question' should be natural language, broad/multi-part is fine; 'depth' quick=3, standard=5, thorough=8 (paid). This goes beyond enum values.

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 'Grounded multi-source research across Pipeworx's 1160 STRUCTURED data sources' and explains decomposition and parallel routing. It distinguishes from siblings by noting it is not open-web search and explicitly contrasts with ask_pipeworx.

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

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

Explicitly says 'Best for broad/multi-part questions over structured data' and provides when-not-to-use: 'For a single lookup use ask_pipeworx', 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx'. Also mentions account requirement and alternative if not signed in.

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, idempotentHint, and destructiveHint. The description adds behavioral context: it returns top-N tools with full schemas ready to call. However, it does not mention pagination or rate limits, which are minor omissions.

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

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

The description is well-structured and front-loaded with purpose. It includes a useful list of domains and clear return value description. While somewhat long, every sentence contributes value.

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

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

Given the tool's complexity (6 params, no output schema), the description is fairly complete. It explains the return format (names, descriptions, schemas) and usage context. Minor improvements could include explaining ranking or limit behavior beyond schema.

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 significant new meaning beyond the schema; it merely restates that query accepts natural language and lists aliases already documented 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 specifies the tool's purpose: 'Find tools by describing the data or task.' It uses a specific verb and resource, and distinguishes itself from sibling tools by advising to call it 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?

It explicitly states when to use: 'Use when you need to browse, search, look up, or discover what tools exist for...' and advises 'Call this FIRST when you have many tools available and want to see the option set.' It lacks explicit exclusion scenarios but provides good context.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds specific details about data sources (SEC EDGAR, XBRL, USPTO, GDELT, GLEIF) and return fields, plus limitations like USPTO sunset. No contradiction with annotations.

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

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

Description is dense but well-structured with examples, caveats, and a clear flow. Every sentence adds value, though it is somewhat long. Front-loading with examples is effective.

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

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

For a tool with 2 required parameters and no output schema, the description comprehensively details input formats, return fields (filings, fundamentals, patents, news, LEI), source dependencies, and limitations. No gaps for an agent to misuse.

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

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

Schema covers both parameters (type, value) with enums and descriptions. Description adds critical context: value must be ticker or zero-padded CIK (not names), and type only 'company' supported. This enriches the schema's meaning, though schema already defines valid values.

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 provides a 'full cross-source profile of a US public company'. Examples like 'Tell me about X' and 'company profile for Microsoft' make the purpose unambiguous. It distinguishes from siblings by mentioning 'resolve_entity' for names and 'compare_entities' implicitly.

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

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

Explicit guidance: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' Also warns that names not supported, directing to use resolve_entity first. Covers fallbacks and soft-fails for patents API.

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, idempotentHint, and destructiveHint. Description adds useful context: fetches directly with proxy fallback if blocked. 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?

Two sentences, front-loaded with core action, zero 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 rich annotations and 100% schema coverage, the description covers essential context: fallback behavior and suggestion to use list_feeds. Lacks output details but acceptable.

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 description does not need to add much. It doesn't provide additional semantics beyond what the schema already describes.

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+resource: 'Fetch and normalize any RSS / Atom / RDF feed by URL.' Distinguishes from sibling tools like list_feeds and read_feed.

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 guidance to 'Use list_feeds first for curated sources,' which clarifies when not to use this tool. Also mentions CF-robust fallback behavior.

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 destructive and idempotent nature; description adds context about stale data and sensitivity, which is valuable beyond annotations.

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

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

Two sentences, action-first, 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?

For a simple deletion tool with one parameter and no output schema, the description is fully adequate, including relationship hints.

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 clear parameter description. The description does not add additional semantics beyond the schema, so baseline score applies.

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 action ('Delete') and the resource ('previously stored memory by key'), effectively distinguishing it from sibling tools like 'remember' and 'recall'.

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

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

Explicitly states when to use (stale context, task done, clear sensitive data) and pairs with siblings, providing clear usage guidance.

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 and idempotentHint. The description adds behavioral context: fetches the page, extracts title/description/key links, and emits standard markdown. No annotation contradiction.

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

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

The description is a single paragraph but well-structured: starts with main purpose, explains output, lists use cases. It is not overly long 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?

Tool has 2 params, no output schema, and rich annotations. Description explains output (text blob) and process. For a simple read-only tool, this is adequately 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% with descriptions for both parameters. The description adds minimal extra meaning (e.g., 'max 50' restates schema). Baseline of 3 is appropriate as schema does the heavy lifting.

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 a production-ready llms.txt file for any URL, specifies the output format (markdown), and lists specific AI crawlers (ChatGPT, Claude, Perplexity). It differentiates from siblings by focusing on a specific standard format.

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

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

The description provides explicit use cases: getting a client's site indexed, drafting for own project, auditing competitor. It does not explicitly state when not to use or name alternatives, but the context is clear given the sibling tools list.

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, idempotentHint, and non-destructive behavior. The description adds no additional behavioral traits beyond listing and filtering, so it does not significantly enhance transparency beyond annotations.

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

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

Two concise sentences with no wasted words. The main action is front-loaded, and the description efficiently conveys key information.

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

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

The description lists returned fields (id, title, category, source), compensating for the lack of an output schema. However, it omits details on pagination or sorting, which could be relevant for a list 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?

All parameters have descriptions in the schema (100% coverage). The description merely restates filtering options, adding no new semantic meaning beyond the schema.

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

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

The description clearly states the tool's purpose: listing curated space & spaceflight feeds with optional filters. It distinguishes from sibling tool 'read_feed' by noting that passing an id leads to that 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?

The description provides implicit guidance by mentioning optional filtering and pointing toward read_feed for details. However, it lacks explicit when-to-use or when-not-to-use context compared to other list-like tools.

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

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

Annotations declare readOnlyHint and idempotentHint; description adds return fields and default behavior (active only). 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?

Two concise sentences: first states purpose and outputs, second gives usage guidance. Front-loaded and 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?

Simple tool with one optional param; description lists return fields and use cases. Annotations cover safety. Complete for its complexity.

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

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

Schema coverage is 100%, so baseline is 3. Description does not elaborate on 'include_inactive' beyond schema, but implies it by mentioning 'active'.

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 'List', resource 'subscriptions', and context 'caller's active subscriptions'. Distinguishes from siblings like subscribe/unsubscribe.

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

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

Explicitly states when to use: 'review what you're monitoring before adding more or to find an id to cancel'. Implies alternatives for adding/cancelling.

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 are all false, so description carries full burden. It discloses rate limit (5 per identifier per day), cost (free, no quota impact), and team reading cadence (daily digests, roadmap impact). This goes well beyond the structured 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 a single focused paragraph that efficiently conveys purpose, usage, and constraints. It front-loads the main action and provides supplementary details without redundancy. Slightly longer than minimal but still 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?

For a feedback tool with 3 parameters (one nested) and no output schema, the description covers purpose, usage guidance, behavioral constraints, and even roadmap impact. It also advises against common misuse. Complete enough for an agent to use correctly.

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

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

Schema description coverage is 100%, so baseline is 3. Description adds high-level context for type usage but doesn't significantly expand on parameter details beyond what's already in the schema. Message length and specificity are already stated in schema.

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

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

Description clearly states the tool is for sending feedback to Pipeworx team about bugs, missing features, etc. It explicitly lists feedback types and distinguishes its purpose from sibling tools, none of which are feedback-related.

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

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

Provides explicit when-to-use guidance for each feedback type (bug, feature, data_gap, praise) and includes what not to do (don't paste end-user prompt). Effectively tells the agent when to choose this tool over 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?

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds valuable context: it states the data source (CF analytics-engine), guarantees no PII, and mentions caching duration (5min-1h depending on window). 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 concise with every sentence serving a purpose: it states the output, lists use cases, and provides internal details. It is front-loaded with the core functionality.

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-only tool with one parameter and no output schema, the description provides sufficient context: purpose, parameter semantics, use cases, caching, data source, and privacy. It is comprehensive.

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

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

The schema covers the parameter fully, but the description adds semantic insight by explaining how different windows affect the results ('shorter windows surface what's hot right now; longer windows show steady-state demand'). This adds value beyond the schema description.

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 'Returns' and the resource 'top tools, top packs, and total call volume'. It specifies the windows (24h, 7d, 30d) and provides distinct use cases that differentiate it from sibling tools like discover_tools.

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

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

The description explicitly lists three concrete scenarios where the tool is useful. However, it does not mention when to avoid using it or explicitly contrast with siblings, which is a minor gap.

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 extensive behavioral traits beyond the annotations, including semantic anchor Jaccard similarity, partition filter for placeholders, fill check against CLOB depth, and detailed response structure. All information is consistent with the readOnlyHint, openWorldHint, idempotentHint 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 the main purpose and well-structured, but it is verbose with technical details. Every sentence serves a purpose, but some could be condensed without losing clarity.

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

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

Given the tool's complexity and lack of output schema, the description fully explains return values, edge cases (skipped_low_similarity, placeholders_filtered), and the fill check. It is complete for an AI agent to select and invoke the tool correctly.

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

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

Although schema coverage is 100% with basic parameter descriptions, the tool's description adds substantial meaning: it explains the purpose of each mode, the algorithms used, expected slugs/topics, and the fill check process, far exceeding 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 finds arbitrage opportunities via monotonicity violations and partition-sum checks. It explains the two modes (trending scan, event, topic) but does not explicitly differentiate from sibling tools like polymarket_edges, though it mentions polymarket_fill_risk for custom sizing.

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 when to use each argument (no args, event, topic) and advises when not to trade via the fill check. It mentions an alternative tool (polymarket_fill_risk) but does not cover all sibling tools.

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

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

Annotations already indicate readOnlyHint, idempotentHint, and non-destructive. The description adds substantial behavioral context: caching at KV level keyed on all knobs, internal model families, output structure with diagnostics, edge calculation net of slippage, and conditions for empty segments. 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 lengthy but well-structured with clear sections for overview, model families, output details, and knob guidance. Given the tool's complexity, the level of detail is appropriate and front-loaded with the 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?

For a tool with 9 parameters, no output schema, and complex internal logic, the description covers everything: purpose, models, response format, diagnostics, caching, and parameter usage. It even explains why segments might be empty, fully preparing 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?

Schema coverage is 100% with detailed parameter descriptions. The description adds practical usage guidance for parameters like min_liquidity ('Set to 5000 to drop thin-book opportunities') and min_partition_leg_kelly, which goes beyond the schema descriptions.

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

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

The description clearly states 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price' and positions it as 'what should I bet on today', distinguishing it from sibling tools like polymarket_arbitrage and polymarket_edge_tracker by focusing on Pipeworx disagreement and model families.

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 the tool is 'Built for "what should I bet on today"' and explains the three segments and tradeable-edge knobs. It warns about Fed bets being excluded. However, it does not explicitly tell the agent when to use this tool over the sibling tools, though it is implied.

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

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

Annotations indicate read-only, idempotent, open-world hints. The description adds rich behavioral context: how snapshots work, gaps meaning no scans, the 60-day TTL limit, and that decay numbers use daily closes not intraday. This far exceeds what annotations alone provide.

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

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

The description is relatively long but well-structured: high-level purpose, argument details, response format. Every sentence adds information; there is little redundancy. Could be slightly tighter but remains effective.

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 thoroughly explains the return object structure (tracked[], expired[], snapshot_dates[]) with details on each field. Combined with full parameter coverage, this is complete for the tool's 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 description coverage is 100%, so baseline is 3. The description adds value by stating defaults (14 for days, '1wk' for window) and clamping range for days (2-30), which is not in the schema. It also explains the 'window' as a snapshot family, adding conceptual clarity.

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 provides edge persistence and decay telemetry from daily snapshots. It distinguishes itself from sibling tools like polymarket_edges by focusing on time-series analysis of edge longevity, using a concrete question: 'how long has this edge existed and is it shrinking?' This is specific and actionable.

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 via the trade comparison, and details the arguments including defaults and limits. However, it does not explicitly mention when not to use it or alternatives like polymarket_edges for current edges without history, leaving slight ambiguity.

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 detailed behavioral traits beyond annotations: it walks the ladder, returns top_of_book, vwap_fill_price, slippage_pp, verdicts (clean/degraded/cannot_fill), and explains basket-mode risks like thin_legs and forced_directional_risk. Annotations already indicate readOnly, idempotent, and non-destructive, which the description aligns with and enriches.

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 lengthy but well-structured with clear sections (REQUIRES, SINGLE-MARKET, BASKET, USE THIS). It front-loads the core purpose and includes essential details. While some redundancy could be trimmed, every sentence serves a purpose, earning a score above average.

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

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

Given the tool's complexity (two modes, multiple return fields, no output schema), the description is remarkably complete. It lists all key outputs for both modes, explains basket risks, and provides actionable thresholds ($500) and failure modes. The description fully compensates for the lack of an output schema.

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 meaning: for side, it explains defaults and auto behavior for baskets; for size_usd, it clarifies interpretation (spend vs target proceeds for single-market, settlement notional for basket) and mentions clamping. This adds value 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: a realizable-vs-theoretical edge check against live CLOB order-book depth. It distinguishes two modes (single-market and basket) and explicitly contrasts with sibling tools like polymarket_arbitrage and polymarket_edges, making its unique role clear.

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 usage guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It explains why (theoretical overround not capturable, partial fills cause unhedged directional risk), offering clear context for when to use this tool versus alternatives.

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

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

Annotations indicate readOnly, idempotent, non-destructive. The description adds extensive behavioral context: explains the 2-25pp spread from different participant pools, details compatibility warnings for non-equivalent bet shapes or unrelated events, temporal alignment info, and skipped cross-type/subtype counters. 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 lengthy and includes many safety field explanations that could be streamlined. It is well-structured and front-loaded with the core purpose, but the latter part is verbose with detailed enumeration of fields and conditions.

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

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

Given the lack of an output schema, the description thoroughly explains the response structure (leg-by-leg prices, top_spreads_pp, compatibility_warning fields, temporal_alignment, counters). It covers edge cases and semantic nuances, making it highly complete for a tool of this complexity.

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

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

Schema coverage is 100% with clear descriptions. The description adds meaning by explaining the two modes, that topic shortcuts auto-fetch matching events, and that explicit parameters override the topic-mapped side. This adds value beyond the schema.

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

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

The description clearly states the tool's purpose as 'Cross-venue spread between Kalshi and Polymarket for the same resolving question' and details two modes: topic shortcuts and explicit tickers. It distinguishes itself from sibling tools by focusing specifically on cross-venue spread analysis.

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

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

The description explains when to use each mode (topic vs explicit tickers) and warns that most pre-mapped topics return compatibility warnings. However, it does not explicitly compare to sibling tools or state when not to use this tool.

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

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds value by detailing what the tool returns (normalized items with specific fields) and the optional filtering capability, 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?

The description is two sentences, directly stating purpose, input source, output format, and optional feature. It is front-loaded with the verb and resource, with no redundant or extraneous information.

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

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

Given the simple tool structure (3 parameters, no output schema, comprehensive annotations), the description covers the essential: what it does, where to get the feed id, what it returns, and a key optional parameter. No critical 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?

The input schema has 100% coverage with descriptions for all parameters. The description adds no new parameter semantics beyond what the schema already provides (e.g., keyword filter over title/summary is repeated). Thus, it meets the baseline for high coverage but does not enhance 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 'read' and the resource 'curated space & spaceflight feed', specifies the input (feed id from list_feeds), and describes the output format. This distinguishes it from siblings like fetch_feed by its curated nature and explicit source.

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 context: you need a feed id from list_feeds to read a specific feed. It mentions optional keyword filtering, guiding when to use that feature. However, it does not explicitly compare to sibling tools like fetch_feed, so some ambiguity remains.

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

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

Adds beyond annotations by describing scoping to user identifier and the dual behavior (retrieve vs list all). No contradictions with annotations (readOnlyHint, idempotentHint).

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, each contributing distinct information: action, use case, scoping, pairing. Front-loaded with core 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 one optional parameter, no output schema, and annotations covering safety, the description fully explains functionality, scoping, and relationship to siblings.

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 adequate parameter description. The description reinforces the omission behavior but does not add novel information beyond the schema.

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

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

Description explicitly states 'Retrieve a value previously saved via remember, or list all saved keys' with specific verb and resource. Distinguishes from siblings by naming 'remember' and 'forget' as paired tools.

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

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

Provides clear when-to-use examples ('look up context the agent stored earlier') and scoping details. Does not explicitly state when not to use, but implies by referencing sibling tools.

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

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

Description contradicts readOnlyHint annotation by declaring mark_read:true flags events as read, a state modification. Annotation contradiction flagged.

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

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

Three sentences, front-loaded with core purpose, no fluff, highly compact and readable.

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

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

No output schema but description lists return fields; covers alternative access (HTTP) and polling usage; fully adequate for 5 optional parameters.

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 examples (sec_8k) and explains mark_read side effects beyond schema descriptions.

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

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

The description clearly states 'Pull fired events from your subscription feed' and lists returned fields, distinguishing from sibling tools like read_feed and list_feeds.

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 filtering by type and since, mark_read behavior, and polling suitability. Mentions alternative HTTP access but lacks explicit when-not-to-use guidance.

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

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

The description goes beyond annotations (which already declare readOnlyHint, idempotentHint, and non-destructive) by detailing the fan-out behavior to multiple data sources (SEC EDGAR, GDELT→GNews fallback, USPTO with soft-fail), providing valuable context for the AI agent.

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

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

The description is a single dense paragraph that is well-structured, starting with example queries and followed by details. While comprehensive, it could be slightly more concise, but every sentence earns its place.

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

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

Despite no output schema, the description explains the return structure (changes[] grouped by source, total_changes, citation URIs). It covers complexity of multiple data sources, fallback, and parameter formatting, making it complete for an AI agent to use 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?

With 100% schema coverage, baseline is 3. The description adds meaning by giving examples for 'since' (ISO date or relative shorthand like '30d') and clarifying that 'value' accepts ticker or CIK. It also notes that 'type' only supports 'company', enhancing understanding beyond the schema.

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

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

The description starts with clear example queries like "What's new with X" and explicitly states it is a change feed for a company in a specified time window. It distinguishes itself from the sibling tool 'entity_profile' by stating when to use the alternative for static profiles.

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 usage guidance: when to use this tool (change feed) vs. 'entity_profile' (static profile), and includes example query patterns. It also specifies 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?

Description adds behavioral details beyond annotations, such as persistence scoping (24-hour vs persistent) and key-value nature. No contradictions with annotations (idempotentHint=true, destructiveHint=false).

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, each providing essential information: purpose, when to use, storage details, and relationship to sibling tools. No redundancy, front-loaded with core 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?

All relevant aspects are covered: purpose, usage, persistence model, authentication impact, and tool pairing. No gaps given the tool's simplicity and schema coverage.

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 clear descriptions for both parameters. Description adds high-level context (key-value pair, scoping) but does not significantly enhance parameter 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 clearly states the tool saves data for later reuse, specifies verb (save) and resource (data), and distinguishes from siblings (recall, forget) by mentioning pairing. It is specific and actionable.

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

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

The description gives explicit usage context: use when discovering something worth carrying forward to avoid re-querying. It also explains behavioral differences based on authentication, effectively guiding when to use the tool.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral details such as internal cascading through multiple endpoints and the specific output structure (e.g., ticker+CIK for company, RxCUI+ingredient for drug, including citation URIs), enhancing transparency.

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

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

The description is well-structured, starting with example usage, then stating purpose, followed by supported types with detailed output. It is slightly verbose but every sentence adds value; minimal 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?

Without an output schema, the description thoroughly explains the return values for each entity type, including citation URIs. It also mentions internal complexity (cascading lookups). Slight room for improvement in explicitly stating what happens on failure (e.g., not found).

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

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

Schema covers 100% of parameters with descriptions, but the description elaborates on parameter behavior (e.g., for 'value', it explains that for company it accepts ticker, CIK, or name, and for drug it accepts brand or generic name, with auto-disambiguation). This adds significant meaning beyond the schema.

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

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

The description explicitly states the tool resolves a user-spoken name to a canonical identifier, with example queries ('What's the ticker for...') and clear supported types. It distinguishes itself from siblings like compare_entities or entity_profile by focusing on ID resolution.

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 advises 'Use FIRST whenever you have a name but need an ID,' providing clear usage context. It also notes that using this tool replaces 2-3 manual lookups, indicating efficiency, though it omits explicit when-not 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 provide readOnlyHint, idempotentHint, etc. Description adds value by explaining it probes each entity with ai_visibility_check, ranks by score, and returns ranked list with score, confidence, signal density. No contradiction.

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

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

Three efficient sentences, front-loaded with core purpose. Every sentence earns its place without redundancy.

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

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

Despite no output schema, description fully describes return format. Covers usage context, behavioral details, and parameter roles. Complete for a complex multi-entity tool.

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

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

Schema coverage is 100% so description carries less burden, but it adds semantic detail: 'First entry treated as the "subject" for narrative; rest are competitors.' This helps the agent choose parameter order.

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 uses specific verb 'Compare' and resource 'AI visibility across multiple entities side-by-side'. Clearly distinguishes from sibling tools like ai_visibility_check (single probe) and compare_entities (different comparison).

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

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

Explicitly states usefulness for competitive AI-marketing audits with example query. Implies when-not-to-use by contrasting with single-entity check. No ambiguity.

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, open-world, idempotent, non-destructive. Description adds behavioral details: partial failures, bundlephobia delay (5-30s), sources_failed reporting. 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 informative but somewhat verbose. However, it is front-loaded with purpose and each sentence adds value. Could be trimmed slightly without losing meaning, but still efficient.

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

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

No output schema exists, so description must explain return values. It lists summary fields, advisory details, links, alternative versions, and handles partial failures. Fully equips agent to understand output.

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

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

Schema has 100% description coverage for both parameters. Package description includes scoped packages, version description notes default to latest. The tool description does not add significant new semantic detail beyond schema.

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

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

Description clearly states it's a composite check for npm packages, combining deps.dev and bundlephobia. Verb+resource+scope are specific. No siblings overlap, so differentiation is inherent.

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

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

Explicitly says when to use: when agent asks about safety, popularity, size, or cost of adding a package. Also mentions NPM-only in v1 and directs alternative ecosystems to deps.dev:version directly. Partial failure handling is documented.

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 adds rich behavioral details beyond the annotations: it explains the embedding model (BGE-base-en), chunking (500-char overlapping windows), character cap (200K with truncation flag), and return components (passages with offsets and similarity scores). Annotations already indicate idempotent and read-only, which are consistent.

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

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

The description is a single dense paragraph that front-loads the purpose. It includes implementation details that might be considered verbose, but every sentence adds value. It is well-structured for an AI agent, though slightly more concise than ideal.

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 explains return values effectively (passages with offsets and scores). It covers constraints, use cases, and pairing with another tool. It is complete enough for an agent to understand invocation and expected results.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds value by giving example queries and re-emphasizing the cap, but does not significantly extend the schema descriptions. The parameter semantics are adequate but not enhanced substantially beyond the schema.

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

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

The description clearly states it performs semantic search inside a fetched record. It specifies the verb 'search' and resource 'inside a fetched record', and provides a concrete use case. However, it does not explicitly distinguish from sibling tools like 'ask_pipeworx' or 'compare_entities', but the unique purpose is still evident.

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 advises using the tool when the record is too large for the prompt, and pairs it with 'ask_pipeworx_grounded'. It provides clear context for when to use, though it does not state when not to use or list alternatives beyond the paired 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?

The description discloses behavioral details beyond annotations: requires OAuth account, phone verification, SMS rate limit of 10/day, webhook signing secret returned once, auto-disable after failures. Annotations already indicate idempotent and non-destructive, so the description adds significant context.

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

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

The description is moderately long but well-structured: purpose first, then requirements, then types, then delivery channels. Every sentence adds value, though more concise phrasing could be used for the delivery section.

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 (3 params, nested objects, no output schema), the description covers all necessary aspects: parameter details, constraints, return value, and differentiates from siblings like list_subscriptions. It is fully complete.

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

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

With 100% schema description coverage, the description still adds substantial meaning: it explains type-specific params with concrete examples for each type, and clarifies delivery channel constraints (phone must be verified, webhook secret one-time).

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 'Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id.' It uses a specific verb-resource pair and distinguishes itself from sibling tools like list_subscriptions and unsubscribe.

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 when to use (monitoring live events), prerequisites (OAuth account, phone verification for SMS), and examples for each subscription type. It does not explicitly state when not to use, but the context is clear.

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

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds value by explaining that it returns 'category-bucketed example questions' with exact tool and argument shapes, drawn from a live catalog. This context enhances understanding of the tool's behavior beyond the annotations.

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

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

The description is relatively long but effectively front-loaded with common user queries and a clear purpose. Every sentence adds value, though it could be slightly more concise. It is well-structured with examples and a call-to-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 no output schema, the description adequately explains the return format (category-bucketed questions with tool shapes). For a simple onboarding tool with one optional parameter, the description is complete and leaves no critical gaps.

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

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

Schema coverage is 100% with one optional parameter (topic). The description enriches the schema by listing example values (finance, pharma, etc.) and explaining the effect of omitting the parameter ('cross-category spread'). This adds meaning beyond the schema's description.

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

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

The description clearly identifies the tool as an onboarding entry point that returns category-bucketed example questions. It includes a list of categories and distinguishes it from siblings like discover_tools by stating 'Use this FIRST when you do not yet know what Pipeworx can do for you.'

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 (as a first step for onboarding) and provides guidance on arguments: 'Call with no arguments for the full spread, or pass topic to focus.' It also mentions using it to learn meta-tools. However, it does not explicitly state when not to use it, which would be ideal for clarity.

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 the tool is not read-only, idempotent, and not destructive. The description adds crucial behavioral context: ownership enforcement (authorization), soft delete (deactivation), and retention of historical data, which go beyond the annotations.

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

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

The description is two sentences long, with the action and primary constraint front-loaded. No extraneous 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?

Given the tool has one parameter, no output schema, and annotations covering safety, the description provides sufficient context: it explains the result (deactivation, not deletion) and links to 'recent_alerts' for history. It is complete for its 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 the schema already describes the 'id' parameter as a UUID returned by 'subscribe'. The description does not add new semantic information about the parameter beyond referencing it 'by id', so it meets but does not exceed 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 uses the verb 'Cancel' and identifies the resource as 'subscription by id', clearly stating the action. It distinguishes from siblings like 'subscribe' and 'list_subscriptions' by specifying the cancellation action and ownership enforcement.

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 that ownership is enforced ('you can only cancel your own subscriptions'), guiding appropriate use. It implies context by mentioning that the row is deactivated and historical events remain available via recent_alerts, but does not explicitly list when not to use or compare to 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 read-only, open-world, idempotent, and non-destructive behavior. The description adds significant context: it uses SEC EDGAR + XBRL, returns a specific verdict structure with citations and percent delta, and highlights efficiency by replacing 4-6 sequential calls. 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 concise yet comprehensive, starting with relatable query examples, then clearly stating purpose, scope, return details, and efficiency benefits. Every sentence adds value with 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?

Despite having no output schema, the description fully explains return types (verdict, structured form, actual value, citation, percent delta), domain (financial claims), and data sources (SEC EDGAR + XBRL). With rich annotations and a single parameter, it provides complete context for an agent to use the tool correctly.

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

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

With 100% schema description coverage, baseline is 3. The description does not add new semantics beyond the schema's parameter description, which already includes examples. The tool-level description restates examples but offers no additional parameter details.

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 defines the tool as for natural-language claim verification, with specific examples ('Is it true that…', 'fact check') and explicitly states it handles company-financial claims via SEC EDGAR + XBRL. It distinguishes its value by noting it replaces multiple sequential calls, setting it apart from siblings that might handle broader research.

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

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

The description explicitly states when to use the tool ('Use whenever the agent needs to check whether something a user said is factually correct') and provides scope (company-financial claims). However, it does not specify when not to use it or mention alternatives among the many sibling tools, missing opportunities for clearer guidance.

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