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

Beyond the readOnly/idempotent annotations, the description discloses that the list may be empty, reveals the exact return object shape, explains the semantics of toolsets and knowledge, and warns that names/descriptions can be misleading. This is rich behavioral context that helps the agent interpret results correctly.

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 longer than typical, but every section earns its place: purpose, return structure, field semantics, routing guidance, and empty-list caveat. The use of bullets and bolded field names keeps it scannable and front-loaded with the 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?

Despite having no output schema, the description fully compensates by detailing the return object, nested structures, and field meanings. It also covers edge cases (empty list, misleading names) and alternatives, making it complete for an agent to invoke and interpret 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?

The schema already fully documents the single search parameter with 100% coverage, including case-insensitivity, substring matching, and omit behavior. The description adds no further parameter-level detail, so the baseline 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 opens with a specific verb and resource: 'List the PipesHub agents configured for this org, each with its capabilities.' It also clearly differentiates this tool from siblings like pipeshub_chat by explaining that this is for listing agents, not conversing with them.

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

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

The description gives explicit routing instructions: use this tool to discover agents, take the agentId, and pass it to pipeshub_chat; for plain Q&A use pipeshub_chat without agentId. It also states when to refrain from forcing an unrelated agent, providing clear when-to-use and 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?

Beyond annotations (readOnlyHint=false, openWorldHint=true, destructiveHint=false), the description discloses key behavioral traits: it only reads retrieved passages, never full documents or complete lists; it undercounts and will not say so; filters are ignored on follow-ups; server-side context is preserved so earlier messages must not be replayed. These are critical limitations not captured by annotations, and they directly inform agent decision-making. 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 long but meticulously structured: core purpose first, then a highlighted 'Three questions this tool gets WRONG' section, followed by 'Everything else... belongs here', internal vs web search, conversation lifecycle, and response handling. Each section is front-loaded with the most important caveats, and every sentence provides actionable guidance. No redundancy or fluff.

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

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

The description covers all aspects needed to use the tool correctly: its scope and limitations, the routing to alternative tools, the distinction between chat modes, the conversation lifecycle, parameter usage, and how to handle the response (capture conversationId, use citations to download documents). No output schema exists, but the description explains the response contains 'answer' plus 'citations'. It is complete for a chat tool with 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?

Although schema coverage is 75%, the description adds substantial semantic depth to parameters. For chatMode it explains the valid values depend on agentId, and that 'quick' is sent automatically when agentId is set. For conversationId it clarifies the lifecycle (omit on first turn, pass on follow-ups, when to re-omit). For filters it notes they only work on the first turn. It also explains how to obtain agentId from pipeshub_agents, and modelKey from pipeshub_sources. This goes far 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 opens with a specific verb-resource pair: 'Ask a question, get an answer grounded in the org's indexed data with citations.' It also explicitly states what it reads ('a few retrieved passages — never a whole document, never a complete list'), which clearly scopes the tool. It further differentiates itself from siblings by naming three categories of questions it handles poorly and directing to the correct alternatives, 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 provides explicit when-to-use and when-not-to-use guidance. It lists three question types (structure, exhaustive, one named document) and directs to specific sibling tools, then enumerates what belongs here (policies, processes, decisions, history). It also distinguishes internal_search vs web_search, explains agent chat modes, and gives a complete conversation lifecycle (first turn vs follow-up, when to restart). No 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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint, but the description adds significant behavioral context: error on expired/revoked credential for whoami, empty arrays for no matches, pagination via hasNextPage/hasNext, and page/limit defaults. No contradictions; the description enriches 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?

Well-structured with a clear opening, bulleted action list, and concise pagination/error notes. Every sentence adds value; no fluff. Front-loaded with the primary purpose and sibling differentiation, making it easy to scan.

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

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

Covers all actions, parameters, error conditions, pagination, and alternatives. Even without an output schema, it describes return arrays (users/groups/teams) and capability flags. Nothing an agent needs to call it correctly is missing.

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 all parameters at 100%, so baseline is 3. The description adds cross-parameter dependencies (userId required for get_user, search only applies to list_* actions), clarifies action-specific requirements, and explains pagination behavior. This goes beyond schema, but the schema already provides solid descriptions, so a 4 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?

States a specific verb and resource: 'Look up people, groups, and teams in PipesHub.' Lists five distinct actions and explicitly differentiates from pipeshub_search for documents/files. An agent can immediately understand scope and distinguish from siblings.

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

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

Explicitly says 'Not for documents or files: that is pipeshub_search,' naming the alternative and when not to use this tool. Also provides per-action guidance, e.g., 'Use before get_user on yourself' and explains pagination defaults and error behavior. Clear when-to-use and when-not-to-use 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 and idempotentHint=true, so the description doesn't need to restate safety. It adds valuable behavioral context: conversion behavior (only application/pdf accepted, bare 'pdf' ignored), output encoding (text inline, binary as base64), and the distinction between stored file and parsed content. No contradiction with annotations. The only minor omission is lack of error handling detail, but that is beyond typical expectations.

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 every sentence serves a distinct purpose: purpose, usage, exclusions, and conversion behavior. It is front-loaded with the core purpose and keeps exclusions and details in later sentences. No filler or redundant phrasing; it earns its length.

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 download tool with two parameters and no output schema, the description covers everything an agent needs: how to obtain the recordId, what convertTo accepts, output encoding, and what this tool is not for. The sibling references complete the routing. Nothing critical is missing.

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 both parameters are already documented. The description adds value beyond the schema: for recordId, it reiterates the source (citation or search) and clarifies the format expectation; for convertTo, it explains that only 'application/pdf' is honored and that 'pdf' is silently ignored. This extra context helps the agent avoid common mistakes.

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

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

The description opens with a clear, specific verb and resource: 'Download the file as stored for one record'. It explicitly contrasts with parsed content, metadata header, and summary, and names the sibling tool for reading content. This fully distinguishes it from pipeshub_get_record_content without needing to inspect 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?

It provides explicit when-to-use guidance ('Use this when the user wants the file itself') and when-not-to-use ('Do not use this to read, summarize, or answer...'), and points to the correct alternative (pipeshub_get_record_content mode:"content"). It also tells the agent where to obtain recordId, leaving no ambiguity about invocation 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?

Beyond annotations (readOnlyHint=true), the description discloses return formats, edge cases ('No record found', '200 with empty matches', 'ambiguous' handling), resolution behavior ('searches ALL connectors'), pagination triggers ('only pass page:2 when Next: line says more'), and rendering details. 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 but every sentence adds practical value for a tool with three modes and 13 parameters. It is front-loaded with a mode summary, uses clear section headers, and avoids filler. The structure mirrors the decision flow an agent needs.

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 complex tool with no output schema, the description fully explains return values for all modes, prerequisites (like obtaining recordId), error/edge behaviors, and sibling routing. Nothing an agent needs to invoke correctly is missing, given the rich schema and annotations.

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%, the description adds significant contextual meaning: explains how to obtain nodeId, when to omit it, how depth affects output, how identifiers resolve automatically, and how to interpret returns like 'Children 1-50 of 61'. These are usage patterns beyond schema field definitions.

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 performs three operations (lookup, navigate, content) on the org's records, with each mode named and explained. It explicitly differentiates from siblings (pipeshub_chat cannot see whole docs, pipeshub_download_record for raw bytes), making 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?

Provides explicit when-to-use guidance per mode, including intent-based examples ('what's this doc about?' → content) and exclusions ('Use pipeshub_download_record only for the original file bytes', 'pipeshub_chat cannot do these'). Also tells when to pass depth and how to get recordId, leaving no ambiguity about selection.

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

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

Beyond the annotations (which are minimal: readOnlyHint=false, openWorldHint=true, idempotentHint=true, destructiveHint=false), the description discloses the key behavioral trait: 'A ranked sample, never a complete list' and explicitly warns against counting hits to answer 'how many'/'all'/'every'. It also explains the hit structure and that one record can appear multiple times, plus how to get the real total (navigate the record group). This adds substantial context the annotations do not cover.

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 long but tightly structured: bolded key points, bulleted typical uses, and a clear 'Not for' section. Every paragraph serves a purpose—purpose, usage guidance, limitations, filtering, and output format. It could be slightly more compact, but the density is justified given the tool's nuanced behavior (sample results, multiple hit sources).

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 there is no output schema, the description fully explains the return format: 'Each hit is one matching passage, best match first: { recordId, recordName, score, snippet, mimeType, webUrl, ... }'. It covers all operational aspects an agent needs: when to use, how to filter, what the results mean, and how to avoid misinterpretation (sample vs complete). Nothing essential is missing.

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%, the description adds meaning beyond the property descriptions: it tells the agent where to obtain ids ('Get them from pipeshub_sources'), clarifies the apps vs kb distinction ('Collection ids go in kb, not here'), and recommends limit values for specific use cases ('Use 5–10 when you only need a recordId'). This goes beyond the schema and genuinely helps parameter selection.

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

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

The description opens with a specific verb+resource ('Vector / semantic search across the org's indexed documents') and immediately states its core use: 'LOCATE a document'. It explicitly contrasts with pipeshub_chat (open-ended questions) and pipeshub_get_record_content (structural questions), making sibling differentiation crystal 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?

It provides explicit when-to-use ('Use this when the user wants to LOCATE a document'), when-not-to-use ('Not for structural questions...'), and direct alternatives ('use pipeshub_chat instead', 'use pipeshub_get_record_content mode:"navigate"'). It also covers filtering by apps/kb and the limit recommendation for recordId retrieval.

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, idempotent, non-destructive behavior. The description adds valuable behavioral context beyond that: the 1,000-source truncation flag, conditional embedding-model fetching, and caching advice. 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 front-loaded with the one-line purpose, then organized into clear bullets for each section. Every sentence earns its place; there is no filler or repetition of annotation data.

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 zero required parameters, no output schema, and comprehensive annotations, the description covers all critical information: what sections exist, their contents, defaults, truncation semantics, downstream usage, and caching recommendation. Nothing necessary for correct invocation is missing.

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?

While the schema already documents the include enum with a default, the description enriches meaning by showing how each section is consumed elsewhere (connector ids in apps, collection ids in kb, modelKey on chat, isDefault selection). This goes beyond the schema's basic parameter documentation.

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 opens with a specific verb and resource: 'Discover available chat sources and AI models in one call.' It then enumerates the three returned sections, each tied to concrete downstream use (e.g., modelKey for pipeshub_chat), clearly distinguishing this discovery tool from its 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?

Gives explicit usage guidance: 'Call this once at the start of a session and cache the result' and says to add embeddingModels only when configuring re-embedding. It also explains how returned ids and modelKeys flow into pipeshub_search and pipeshub_chat, so an agent knows exactly when to invoke this tool.

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