Slack_connect

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

Description adds value beyond annotations: explains call flow (probes multiple models), return structure (per-model score, confidence, etc.), and cost implications for Anthropic. 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?

Single paragraph packs key info (purpose, models, cost, output) efficiently. Minor improvement: bullet points could enhance scannability, but current structure is adequate.

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 values (per-model fields + combined view). Covers models, authentication, and example usage. 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 coverage is 100% with descriptions, and description adds context: default model, _apiKey passthrough, context disambiguation. Exceeds baseline (3) by explaining behavior beyond schema.

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

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

The description uses the verb 'probe' and specifies the resource 'LLMs' with a defined outcome 'score visibility (0-100) per model'. It clearly distinguishes from siblings like 'compare_entities' by focusing on AI awareness of an entity.

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

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

Explicitly lists use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring). Mentions default model and BYO key for Anthropic. Could be improved by stating when not to use, but provides clear 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that the tool picks the right tool, fills arguments, and returns results, which is transparent about its internal behavior. However, it doesn't disclose limitations or what happens if no data source is available.

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, front-loaded with the key action, and includes examples for clarity. Every sentence adds value without redundancy.

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

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

Given the tool's simplicity (one required parameter, no output schema, no nested objects), the description is complete enough. It explains what the tool does and how to use it. However, it could be more complete by mentioning that it may not work for very specific or ambiguous queries.

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 coverage is 100% with one parameter (question) described as 'Your question or request in natural language'. The description adds value by explaining how the parameter is used in context: asking a question in plain English. This provides meaning beyond the schema's brief 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 tool's purpose: ask a question in plain English and get an answer from the best available data source. It distinguishes itself from sibling tools by emphasizing natural language querying without needing to know specific tools or schemas.

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: just describe what you need, and it provides examples. It implies that this tool is for high-level queries and may obviate the need for other tools, but does not explicitly state when not to use it or mention alternatives.

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

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

Annotations confirm read-only, non-destructive behavior; description aligns. Adds value by detailing internal steps (resolves market, classifies bet, fans out to packs). Does not disclose failure modes or rate limits, but given annotations, it is adequately transparent.

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

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

Three sentences, information-dense without being verbose. Front-loads purpose and follows with details. Could be slightly tighter, but overall well-structured.

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

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

Given the complexity and lack of output schema, description sufficiently explains what the tool returns (evidence packet + market-vs-model comparison), its value proposition, and how it classifies bets. Leaves no major gaps for agent understanding.

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

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

Input schema covers both parameters with descriptions. Description supplements by explaining the three formats for market (slug, URL, question) and depth options (quick vs thorough with evidence counts). Adds meaningful context 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 researches Polymarket bets by pulling Pipeworx data. Specifies verb (research), resource (Polymarket bet), and outcomes (evidence packet + comparison). Distinguishes from sibling tools like ask_pipeworx or validate_claim by its specific focus on betting edge.

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 usage context: for questions like 'should I bet on X?' or 'is there edge?'. Contrasts with the alternative of manually discovering packs, implying this tool is more efficient. Does not explicitly list when not to use or other alternatives, but gives sufficient 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?

With no annotations, the description carries full burden for behavioral traits. It notes the tool is read-only and returns structured data with resource URIs, but omits details on error behavior, rate limits, or data freshness. Adequate but not comprehensive.

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

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

Two sentences, no filler. The first sentence states core purpose and scope; the second details type-specific outputs and a key benefit. 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 adequately explains return data (paired data + URIs) and the fields per entity type. It could be strengthened by describing the structure of paired data or potential failure modes, but overall covers essential context for a comparison 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 has 100% coverage, baseline 3. The description adds value by clarifying how values map to entity types (tickers/CIKs vs drug names) and specifying the supported count range (2-5), which is not fully evident from the schema's enum descriptions alone.

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

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

The description uses a specific verb 'Compare' and resource 'entities', explicitly states entity types (company/drug) and the data fields retrieved for each. It clearly distinguishes from sibling tools like 'resolve_entity' or 'ask_pipeworx' by focusing on side-by-side comparison.

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

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

The description implies usage context (comparing 2-5 entities) and highlights efficiency benefits over sequential calls. However, it lacks explicit when-not-to-use guidance or alternative tool suggestions, which would improve this score.

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

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

No annotations provided, so description carries full burden. Discloses that it returns 'most relevant tools with names and descriptions,' which is helpful but does not mention sorting, ranking, or any side effects. Acceptable but minimal.

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

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

Two sentences with no wasted words. First sentence states purpose, second gives usage advice. Could be slightly more structured but very concise.

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

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

Given the tool's simplicity (search with query and limit), no output schema, and no annotations, the description covers the essential aspects. It could mention default limit and max limit (already in schema), but overall complete for this use case.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining that the query is a 'natural language description' and gives concrete examples, which helps the agent understand parameter intent beyond the schema.

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

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

Clearly states it searches the Pipeworx tool catalog by describing what you need, returns relevant tools, and advises to call this first when many tools are available. Specific verb 'search' and resource 'tool catalog' with clear purpose.

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

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

Explicitly says 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task,' providing clear when-to-use guidance. No alternatives mentioned but context makes it obvious.

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

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

No annotations provided, so description carries full burden. It mentions the tool returns pipeworx:// citation URIs and replaces 10-15 sequential calls, but does not explicitly state safety (read-only) or other behavioral traits like auth needs or rate limits.

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

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

The description is three sentences with no wasted words. It front-loads the purpose, then details contents, then provides alternative usage information, making it easy to parse.

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 two parameters, full schema coverage, and no output schema, the description adequately explains what the tool does, what it returns, and when to use alternatives. It lacks explicit mention of read-only behavior but is otherwise complete.

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

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

Schema coverage is 100%, and the description adds meaningful context for the 'value' parameter by specifying accepted formats (ticker or zero-padded CIK) and clarifying that names are not supported, which goes 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 it returns a full profile of an entity across relevant Pipeworx packs, lists specific data sources (SEC filings, XBRL, patents, news, LEI), and distinguishes itself from siblings like resolve_entity by mentioning name resolution is not supported and should be done beforehand.

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 this tool (for company profiles) and when not to (for federal contracts, use usa_recipient_profile directly). Also recommends using resolve_entity if only a name is available, providing clear guidance on alternatives.

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

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

No annotations exist, so the description must carry the burden. It indicates a destructive action (delete) but does not mention if deletion is irreversible, cascading effects, or permissions needed. Adequate but not rich.

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

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

One short sentence that is front-loaded with the action and object. No wasted words.

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

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

Given the tool's simplicity (1 param, no output schema, no annotations), the description is functionally adequate but could mention return value (e.g., confirmation message) or safety notes. Average.

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

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

Schema coverage is 100% with a single required 'key' parameter described. The description reinforces that the key identifies the memory to delete, adding no extra meaning beyond the schema, but schema coverage is high so baseline is 3; slight bonus for clarity in context.

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

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

The description clearly states the action (delete) and the resource (stored memory) and specifies the parameter (key). However, it does not distinguish from sibling tools like 'recall' and 'remember', which might be 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?

The description provides no guidance on when to use this tool versus alternatives like 'recall' (retrieval) or 'remember' (storage). No when-not-to-use or exclusions are given.

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 is read-only, idempotent, and non-destructive. The description adds the internal steps (fetch page, extract title/description/links) and output format (single text blob for site-root/llms.txt), 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?

Three sentences with front-loaded purpose, followed by process and use cases. No wasted words; each 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?

Given two simple parameters and no output schema, the description covers the tool's action, output, and common use cases. Missing potential error handling or network concerns, but is largely complete for the complexity level.

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?

Both parameters have schemas with descriptions (100% coverage). The description does not add new parameter meaning beyond what the schema provides; it merely restates the URL's purpose and default/max for max_links, aligning with 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 verb 'Generate' and resource 'llms.txt file for any URL', and it distinguishes from diverse siblings by focusing on a unique AI indexing task. It specifies the process (fetch page, extract, emit) and target consumers.

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

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

Explicitly lists three use cases (client site indexing, own project drafting, competitor auditing), giving clear context. Does not mention exclusions or alternatives, but sibling tools are unrelated so differentiation 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?

With no annotations, the description fully covers behavioral traits: rate-limited to 5 messages per identifier per day, free to use, and instructions to avoid including end-user prompts verbatim. No contradictions or missing critical disclosures.

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 concise sentences: first states purpose, second lists use cases and content guidelines, third provides constraints. 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?

The description covers purpose, usage, constraints, and parameter hints adequately. No output schema exists, so return values are not expected. The tool is simple and the description is sufficient for correct invocation without additional context.

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

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

Schema description coverage is 100% (all parameters documented). The description adds value beyond schema by advising on message content ('do not include end-user's prompt verbatim') and specifying the rate limit, which aids proper use.

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

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

The description clearly states 'Send feedback to the Pipeworx team' and enumerates specific use cases (bug reports, feature requests, missing data, praise). It is distinct from sibling tools like ask_pipeworx or discover_tools, which serve different purposes.

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

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

Provides explicit guidance on when to use (bug reports, feature requests, etc.), what to include (describe using Pipeworx tools/data), and constraints (rate limit of 5 per day, free). Lacks explicit 'when not to use' or alternative tool mentions, but the purpose is specific enough.

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

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

Annotations already indicate readOnlyHint=true and openWorldHint=true. The description adds substantial behavioral context: walks child markets, extracts dates/thresholds, sorts, and reports violations. It explains the underlying arbitrage rule, which goes well 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 somewhat long but well-structured: it front-loads the purpose, explains the arbitrage logic, usage, and return format. Every sentence adds value, though some slight trimming might be possible without loss.

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 (arbitrage detection with multiple child markets) and lack of output schema, the description is very complete. It explains the rule, the process, and the return structure (list of market pairs with details). No obvious 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 for the single 'event' parameter. The description repeats the schema's description ('Pass a Polymarket event slug or URL'). Since schema coverage is high, the description does not add significant new semantic meaning beyond what is already in the schema.

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

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

The description clearly states the tool's purpose: 'Find arbitrage opportunities within a Polymarket event by checking for monotonicity violations.' It provides a specific verb (find), resource (arbitrage opportunities within a Polymarket event), and distinguishes itself from sibling tools by focusing on monotonicity in date-ordered markets.

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: for events with multiple 'by date' or 'by threshold' markets. It implicitly says not to use if no such markets exist, but does not explicitly mention alternatives or contrast with sibling tools like 'bet_research'. The guidance is clear but lacks exclusion criteria.

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

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

Annotations already declare readOnly and nondestructive. Description adds context: model details (lognormal from FRED + coinpaprika), grouping by asset, single fetch of price history, ranking by edge magnitude. Doesn't mention limitations like only crypto-price bets.

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 detailed but front-loads purpose. Every sentence adds value; slightly verbose but not wasteful. Could be tightened slightly but maintains 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 no output schema, the description sufficiently explains return (top N by edge magnitude with trade direction). Covers algorithm, inputs, and intent. Complete for a list-finding tool with model-based logic.

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 descriptions in schema are good. The description restates defaults (limit 10, window 1wk, min_edge_pp 0.5) but does not add meaningful additional semantics beyond what the schema provides.

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

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

The description clearly states the tool scans high-volume Polymarket markets to find where Pipeworx data disagrees with market price, returning ranked edges. It uses specific verbs and resource, and distinguishes from siblings like polymarket_arbitrage.

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

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

Explicitly states the tool is for the 'what should I bet on today' question, helping users discover opportunities without manual paging. Implicitly excludes arbitrage use cases, 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?

No annotations provided, so description must carry the burden. Clearly states it is a read operation (retrieve/list) and mentions cross-session persistence. 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 with clear purpose and usage. No wasted words.

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

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

Given simple schema (1 optional param) and no output schema, description sufficiently covers the behavior. Could mention return format but not necessary for clarity.

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. Description adds value by clarifying that omitting key lists all, but does not add details about format or behavior 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?

Clearly states the tool retrieves a memory by key or lists all if key is omitted. Distinguishes from 'remember' and 'forget' siblings.

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

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

Explicitly says when to use ('retrieve context you saved earlier'), but does not mention when not to use or alternatives.

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

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

No annotations are provided, so the description bears the full burden. It discloses the parallel fan-out behavior, accepted date formats (ISO and relative), and return value structure (structured changes, count, URIs). However, it does not explicitly state that the operation is read-only or mention any rate limits or authentication needs.

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 (3-4 sentences) and front-loaded with the core purpose. Every sentence adds meaningful information without redundancy or filler.

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

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

Given the tool's complexity (parallel fan-out to multiple sources), the description adequately covers the return shape (structured changes + total_changes + URIs). However, since there is no output schema, a bit more detail about the specific fields in the structured changes would improve completeness.

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

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

Schema coverage is 100%, but the description adds significant value beyond the schema by explaining the 'since' parameter formats with examples, clarifying that 'value' can be a ticker or CIK, and emphasizing that 'type' only supports 'company'. This helps an agent understand how to populate parameters correctly.

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

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

The description clearly states the tool's purpose: 'What's new about an entity since a given point in time.' It specifies the supported entity type ('company') and the parallel data sources (SEC EDGAR, GDELT, USPTO), making it distinct from sibling tools like 'entity_profile' or '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?

The description provides explicit use cases: 'Use for brief me on what happened with X or change-monitoring workflows.' While it does not explicitly state when not to use the tool or mention alternatives, the context is clear and sufficient for an agent to decide when to invoke 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?

Discloses persistence behavior: authenticated users get persistent memory, anonymous sessions last 24 hours. No annotations provided, so description carries full burden; it does well.

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 concise sentences, each serving a purpose: what it does, when to use, and behavioral note. No wasted words.

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

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

Given no output schema and simple key-value storage, description is complete. Explains memory persistence. Could mention return value (e.g., success/failure) but not critical.

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 good descriptions. Description adds usage examples for keys, but not essential beyond schema. Baseline 3 is appropriate.

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

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

Clearly states verb 'store' and resource 'key-value pair in session memory'. Differentiates from sibling 'recall' and 'forget' by specifying the action of saving data.

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

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

Explicitly says 'use this to save intermediate findings, user preferences, or context across tool calls', providing clear use cases. Does not explicitly state when not to use, but context is clear.

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

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

No annotations are provided, so the description carries full burden. It transparently details the input formats (ticker, CIK, or name for type='company'), output fields (ticker, CIK, company name, resource URIs), and version scope. It implies a read-only operation without explicit safety statements, which is acceptable for a lookup tool. The description adds sufficient behavioral context beyond the schema.

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 compact (three sentences), with the primary action front-loaded in the first sentence. Every sentence adds value: purpose, input forms, output, and efficiency improvement. No redundancy or filler.

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

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

Given the simplicity of the tool (2 parameters, no output schema), the description is fully complete. It covers purpose, input syntax, output contents, versioning, and efficiency context. An agent has all necessary information to invoke the tool correctly without additional metadata.

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

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

Schema description coverage is 100%, but the description adds meaningful examples and context (e.g., 'AAPL', '0000320193', 'Apple') and specifies the version limitation for the 'type' parameter. This goes beyond the schema by clarifying acceptable formats and demonstrating usage, earning a score above the baseline of 3.

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

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

The description clearly states the tool's purpose: 'Resolve an entity to canonical IDs across Pipeworx data sources in a single call.' It specifies the action (resolve), resource (entity), and result (canonical IDs), and includes version details for the 'company' type. It distinguishes itself from sibling tools that are unrelated (e.g., chat, memory, Slack 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 provides contextual usage by stating it 'Replaces 2–3 lookup calls,' implying efficiency gains over alternatives. However, it does not explicitly state when not to use this tool or name specific alternative tools among siblings (though siblings are functionally distinct, so this is not a major gap). The guidance is clear enough for an agent to understand its value proposition.

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 no destruction. The description adds that the tool probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with metrics. 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 purpose, no unnecessary words. Each sentence adds information: purpose, use case, and output structure.

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 moderate complexity and no output schema, the description adequately describes the input parameters and the output format (ranked list with score, confidence, signal density). Annotations cover safety and idempotency.

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

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

Schema coverage is 100%, but the description adds value by explaining that the first entity in the array is treated as the subject for narrative, and provides example context for the 'context' parameter.

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

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

The description clearly states the tool compares AI visibility across multiple entities, ranks them, and surfaces which is most/least recognized. It distinguishes itself from the sibling 'ai_visibility_check' which is for single entity probes.

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 a clear use case ('competitive AI-marketing audits') and an example question. It implicitly contrasts with the single-entity sibling tool, but does not explicitly state when not to use it or list 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?

The description discloses the important behavioral trait that the bot will auto-join the channel if it is not already a member. This goes beyond what annotations provide (none) and adds practical context for the agent. The mention of auto-joining is a significant behavioral detail.

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

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

The description is two concise sentences. The first sentence states the core purpose, and the second adds a critical behavioral note. No filler or redundancy.

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

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

Given no output schema, the description does not explain return values, which is a minor gap. However, the tool is a straightforward history retrieval, and the auto-join note adds completeness. The parameter schema is fully described, so the description is fairly complete.

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

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

Schema description coverage is 100%, so the schema already documents all five parameters thoroughly. The description adds no additional parameter meaning beyond what is in the schema. Baseline 3 is correct.

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

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

The description clearly states the tool retrieves message history from a Slack channel. The verb 'Get' and resource 'message history' are specific, and it distinguishes itself from sibling tools like slack_send_message, slack_join_channel, and slack_list_channels.

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 notes that the bot auto-joins the channel if needed, which helps the agent understand when to use this tool even if not a member. However, it does not explicitly contrast with slack_join_channel or provide when-not-to-use guidance, so a 4 is appropriate.

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

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

The description discloses that the bot will be able to read history and post messages after joining, which is behavioral information beyond what annotations provide (none exist). It does not mention potential side effects (e.g., notifications to members) or permissions needed, but for a simple join operation, this is adequate.

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

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

The description is a single, clear sentence that front-loads the action and purpose. No wasted words; every part 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's simplicity (one required parameter, no output schema, no nested objects), the description adequately covers the purpose and outcome. It could mention that the bot must be invited or have permissions, but for a basic join operation, it is complete enough.

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

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

Schema description coverage is 100%, with the parameter 'channel' described as 'Channel ID to join.' The description adds no additional meaning to the parameter beyond the schema. Since coverage is high, the baseline is 3, but the description provides useful context about why joining is needed (reading history and posting), earning a 4.

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

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

The description uses a specific verb ('Join') and resource ('public Slack channel'), and clearly states the purpose: 'so the bot can read history and post messages.' This distinguishes it from siblings like 'slack_send_message' (which posts without joining) and 'slack_channel_history' (which reads without joining).

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

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

The description implies when to use this tool (when the bot needs to read history and post messages in a channel) but does not explicitly state when not to use it or mention alternatives. Given sibling tools like 'slack_send_message' and 'slack_channel_history', the context is clear, but explicit exclusion would improve the score.

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

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

Annotations are empty, so description must cover behavioral traits. It does not mention that listing is a read-only operation, pagination behavior (cursor usage), or rate limits. It adds no transparency beyond the basic function.

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

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

Two sentences, front-loaded with action and resource. Concise and to the point, no wasted words.

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

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

Given that there is no output schema, the description should clarify return format beyond names/IDs/metadata. It omits pagination details and doesn't explain cursor usage. For a simple list tool, it is somewhat incomplete.

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

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

Schema coverage is 100%, so parameters are well-documented in schema. The description adds no additional meaning to parameters; it only summarizes the overall function. Baseline score of 3 is appropriate.

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

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

The description clearly states the verb 'List' and resource 'channels in the Slack workspace', and mentions returned data (names, IDs, metadata). It is distinct from sibling tools like 'slack_channel_history' which retrieves messages, or 'slack_list_users' which lists users. However, it does not explicitly contrast with siblings.

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

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

No guidance on when to use this tool versus alternatives like 'slack_channel_history' or 'slack_send_message'. There is no mention of prerequisites, limitations, or when not to use it.

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

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

No annotations provided, so description carries full burden. It states it's a read operation (list) and returns specific data (profiles, IDs, status). No mention of pagination behavior beyond schema parameters, rate limits, or permissions. Adequate but could elaborate on data freshness or scope.

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, concise and to the point. Front-loaded with action and target. No wasted words. Could add one more detail without being verbose.

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

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

Simple tool with few parameters and no output schema; description covers basic purpose and return info. Lacks guidance on when to use pagination or limit parameter. Adequate for a straightforward listing tool.

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

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

Schema coverage is 100%, so baseline is 3. Description doesn't add meaning beyond schema; it mentions 'profiles, IDs, and status' as return values but doesn't detail parameters. No parameter-specific info in 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?

Description clearly states it lists users in the Slack workspace and returns profiles, IDs, and status. It distinguishes from sibling tools like slack_send_message and slack_channel_history, though could be more specific about scope (e.g., all users vs. filtered).

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

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

Implied usage is retrieving user info, but no explicit guidance on when to use this vs. other tools. No alternatives or when-not-to-use mentioned. Sibling tools like slack_list_channels have different purposes, but no direct comparison.

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 explicitly reveals the auto-join behavior, which is not evident from annotations or schema. Since annotations are empty, the description carries the full burden, and it provides valuable behavioral context beyond the schema.

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. Front-loaded with the main action, then the behavioral note.

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

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

Given no output schema and empty annotations, the description provides the key behavior (auto-join) and parameter hints (markdown). However, it could mention that the bot must be in the workspace, or any limitations on message length or formatting.

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

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

Schema coverage is 100%, so the description adds minimal parameter meaning. The note about Slack markdown adds value for the text parameter, but overall the description does not elaborate on channel format or thread_ts usage beyond schema.

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

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

The description clearly states the verb 'Send a message' and the resource 'Slack channel', with a unique behavioral note about bot auto-joining. It distinguishes from siblings like slack_channel_history (reading history) and slack_join_channel (joining only).

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

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

The description implies when to use this tool (to send a message) and the auto-join feature guides usage for non-member channels. However, it does not explicitly state when not to use it or mention alternatives, though the sibling list provides context.

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

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

No annotations are provided, so the description must carry the burden of behavioral transparency. It discloses that the tool returns a verdict, structured form, actual value with citation, and percent delta, which informs the agent about the output. However, it does not mention any side effects, authentication needs, or whether the tool is read-only. This is a minor gap given the tool appears to be a query-only operation.

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 four sentences, each adding distinct value: purpose, domain/outputs, and efficiency. It is front-loaded with the core purpose. No unnecessary information is present, though the versioning detail ('v1') could be integrated without loss. Overall, it is well-structured and efficient.

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

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

Given that there are no annotations and no output schema, the description covers the essential aspects: what the tool does, what inputs are expected, what outputs are produced, and the context of use (financial claims). It lacks a statement about read-only or mutability, but for a single-param tool, the description is fairly complete and informative for an agent.

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

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

The input schema already covers the single parameter 'claim' with a description and examples, achieving 100% schema coverage. The description adds context by specifying the supported claim types (revenue, net income, cash) and how the output ties back to the input. While this is helpful, it does not significantly extend the parameter's meaning beyond what the schema provides, keeping the score at baseline 3.

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

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

The description clearly states the tool's purpose: fact-check a natural-language claim against authoritative sources. It specifies the supported domain (company-financial claims for US public companies) and lists the types of claims (revenue, net income, cash). This clearly distinguishes it from sibling tools like ask_pipeworx or compare_entities, which have different purposes.

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

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

The description explicitly says 'v1 supports company-financial claims (revenue / net income / cash for public US companies)', which tells the agent when to use the tool. It also mentions that it replaces 4-6 sequential agent calls, implying it is more efficient for this task. However, it does not explicitly state when NOT to use it or list alternative tools for non-financial claims, which would improve clarity.

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