Userology MCP Server

Official

by Userology-Inc

Overview Schema Related Servers Score Discussions

Python

Remote

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have clearly distinct purposes, with descriptions carefully differentiating between similar get_* analytics tools (e.g., get_analytics_summary vs get_study_insights vs get_sessions_report). The get_study vs get_study_summary pair is explicitly disambiguated, and report tools are scoped by participant/session/study. Only minor overlap exists among the various analytics read tools, but the guidance is strong.
Naming Consistency4/5
The server follows a consistent get_*/create_*/update_*/delete_*/generate_* pattern, making most tool names predictable. Minor inconsistencies exist: list_studies vs get_sections/get_participants, and copy_study uses a verb not aligned with the standard create/update/delete set. Overall, the naming is coherent and readable.
Tool Count2/5
With 42 tools, this server exceeds the 'too many' threshold of 25+ for the apparent scope. While the domain (UX research) is broad, many specialized analytics read tools (e.g., get_qualitative_insights, get_quantitative_data, get_qna_analysis) could potentially be consolidated. The high count will likely overwhelm agents and increase selection difficulty.
Completeness4/5
The tool set is very comprehensive: full study CRUD, sections with guide generation/editing, participant and session retrieval, analytics generation and multiple read views, report generation, synthesis chat, tags, and Figma integration. Minor gaps exist (e.g., no section reordering, no participant CRUD), but core workflows have no dead ends and dependencies are clearly documented.
Average 4.4/5 across 42 of 42 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No high-severity vulnerability alerts
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This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only states the operation ('Get all tags') without mentioning return format, edge cases (e.g., no tags), side effects, or whether it is read-only (though 'Get' implies it). This is a minimal disclosure for a tool without annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized, with the primary purpose front-loaded in the first sentence. The Args block is concise and necessary given the missing schema descriptions. Every word earns its place, and the structure is clean.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read tool with one parameter and an output schema, the description covers the core purpose and parameter adequately. The presence of an output schema mitigates the need for return-value documentation. However, it lacks behavioral details such as empty-result behavior or ordering, and does not reference alternative tools, slightly reducing completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must compensate. The Args section defines study_id as 'The unique identifier of the study', adding basic semantic meaning beyond the schema's title. However, it lacks details about the ID format or how to obtain it, leaving the parameter description somewhat generic.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Get' and a clear resource 'all tags assigned to a study', stating the operation directly. It does not explicitly differentiate from sibling tools like update_study_tags, but the name and description make the distinction obvious.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'useful for organizing and filtering studies' implies a use case but does not explicitly specify when to use this tool versus alternatives such as get_study or update_study_tags. No exclusions or when-not-to-use guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility for disclosing behavior. It only says 'update' without explaining whether it is a partial update, what happens to other study fields, or any permission requirements. The output schema exists but is not referenced, adding no behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and well-organized: a single purpose sentence followed by a clean argument list. Every line earns its place, and the arg list is essential given the schema's lack of descriptions. No waste or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple, but the description only covers purpose and parameters. It lacks usage guidelines (vs siblings) and behavioral transparency (e.g., partial update semantics). The output schema may cover return values, but the description does not stand alone well for all decision needs, so it is minimally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates fully by providing clear, explicit explanations for all four parameters: study_id, objective, user_details, and product_overview. This leaves no ambiguity about parameter meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool updates study details and lists specific fields (objective, user details, product overview). It is distinguishable from siblings like update_study_name and update_study_status by the field list, but does not explicitly differentiate, so it stops short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives. The sibling list includes update_study_name and update_study_status, but the description lacks any mention of when to choose this tool or how it differs, leaving the agent without selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only restates the action and parameter definitions, adding no context about allowed status transitions, validation rules, side effects, or permission requirements. It provides no extra behavioral insight beyond the function name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, using a clean purpose statement followed by a standard Args block. Every element serves a purpose, and there is no wasted language or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a state-changing operation, the description is minimally complete for parameter understanding but lacks crucial context about study lifecycle rules, such as whether transitions between specific statuses are restricted or what the output schema contains. Given the existence of an output schema and sibling update tools, more behavioral context is needed for safe and correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, but the description compensates by defining study_id as 'the unique identifier of the study' and status with the enumerated values 'Draft', 'Active', 'Paused', 'Completed'. This gives agents the controlled vocabulary missing from the schema, though it could add format details for study_id.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Change the status of a study' with a specific verb and resource, effectively distinguishing it from sibling tools like update_study_name and update_study_details. It is unambiguous and directly describes the tool's function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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, nor any context about study lifecycle workflows, prerequisites, or exclusions. It does not mention how this differs from other update tools or when a status change is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 only says 'Rename a study' without mentioning side effects, permissions, idempotency, or potential impacts on related data. This is insufficient for a mutation operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is minimal and well-structured: a one-sentence summary followed by a clear argument list. No filler or redundant content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple and an output schema exists, so return values are covered. However, the description lacks context about behavioral implications, restrictions (e.g., name uniqueness), or relationship to other study update tools, leaving moderate gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description's Args section adds meaningful semantics beyond the raw schema: study_id is described as 'the unique identifier' and name as 'the new name.' Since schema coverage is 0%, this compensation is valuable and covers both parameters adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb and resource: 'Rename a study.' This distinguishes it from sibling tools like update_study_details, update_study_status, and update_study_tags, which handle other study updates.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. There is no mention of exclusions or preferred scenarios, leaving the agent to infer usage solely from the tool name and generic 'rename' action.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description does not disclose key behavioral aspects, such as whether the tags list is replaced or merged, whether the study must exist, or any validation rules. Since no annotations are provided, the description carries the full burden but falls short.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact: a one-sentence imperative followed by a clear Args block. Every sentence adds value, and the structure is easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite being simple, a mutation tool with no annotations needs to clarify whether tags are replaced or appended, and what the return value or side effects are. The description does not provide this, leaving important context missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Both parameters receive useful explanations beyond the schema titles: study_id is described as 'the unique identifier of the study,' and tags as a 'comma-separated list' with a concrete example. This meaningfully adds to the input schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action: 'Update the tags for a study.' This is a specific verb (update) targeting a specific resource (tags of a study), making it easy to distinguish from siblings like get_study_tags or update_study_name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit guidance on when to use this tool versus alternatives, nor any exclusions. However, the purpose is self-explanatory for updating study tags, so usage is implied but not explicitly contrasted with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the update action but does not clarify whether it performs a partial or full update, what happens when the section doesn't exist, or any side effects/permissions required.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a clear opening sentence followed by a parameter list. The list is necessary because the schema lacks descriptions, so every line earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 6 parameters and is a mutating operation with no annotations or schema-level descriptions. The description covers purpose and parameters, but lacks behavioral details (e.g., partial update behavior, existence checks) and usage prerequisites, leaving some gaps for a complete understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description provides one-line explanations for each parameter (e.g., 'The unique identifier of the study'), adding meaning that the input schema lacks (schema_description_coverage is 0%). It clarifies the intended format for prototype_link and figma_link, though it doesn't address edge cases like empty-string semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb ('Update'), a resource ('section'), and the exact fields affected (name, description, prototype_link, figma_link). This distinguishes it from sibling tools like create_section, get_section, and delete_section.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for updating an existing section's attributes, which is clear context. However, it does not explicitly mention exclusions or when to use alternatives like create_section or get_section.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing behavior. It states the return type ('video and/or audio stream/download links') but does not mention potential errors, empty results, authorization needs, or any side effects. The guidance is adequate 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a clear purpose statement, a follow-up on return value, a practical prerequisite hint, and a compact Args list. Every sentence adds value and the layout is easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so the tool description need not detail return values. The prerequisite call to get_participants is included, and all three required parameters are listed. For a straightforward retrieval tool with three parameters, the description is sufficiently complete, though it could mention handling of missing recordings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description's Args section must compensate. It provides simple definitions ('unique identifier') that are largely redundant with the parameter names. The hint to use get_participants to obtain participant_id and session_id adds practical value, but study_id remains underexplained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Get recording URLs (video/audio) for a specific session,' clearly specifying the action and resource. It distinguishes itself from sibling tools like get_session_report or get_session by focusing on recording media links, making the tool's purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly instructs users to 'Call get_participants first to find the correct participant_id and session_id,' providing a clear prerequisite and workflow context. It does not, however, enumerate alternative tools or explicitly state when not to use this tool, so it stops short of full exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full disclosure burden. It adds the key behavioral detail of returning 'full message history,' which is useful. However, it does not discuss immutability, potential errors (e.g., chat not found), authentication, or other side effects, though for a simple getter the risk is low. This is 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, with a clear opening sentence stating the tool's function, followed by a brief usage note and an Args block. Every sentence adds value, and the structure is easy to scan. No unnecessary repetition or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter, simple retrieval) and the presence of an output schema, the description covers the essential aspects: purpose, use cases, and parameter. It does not describe the return structure in detail, but the output schema already handles that. Minor gaps like error handling and pagination are not critical here.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate for the parameter's meaning. It includes 'Args:' with a one-line explanation that chat_id is 'The unique identifier of the chat.' This is minimal but sufficient for a single, obvious parameter; it does not add format details or retrieval guidance, so it stays at a baseline 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves a specific Synthesis Studio chat with its full message history. This distinguishes it from sibling tools like list_synthesis_chats (which lists chats) and delete_synthesis_chat (which removes a chat), and the verb 'get' precisely indicates a read operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides concrete use cases: reviewing a previous conversation, showing asked questions, or resuming an analysis session. This gives clear context on when to use the tool, but it does not explicitly mention when not to use it or name alternative tools (e.g., list_synthesis_chats for browsing chats), so it misses the top score for exclusions/alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It states the primary effect (public access) but fails to mention reversibility, required permissions, or whether this alters the chat permanently. For a mutating access-control action, this transparency gap is significant.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: two sentences conveying purpose and return value, followed by a clear Args block. It is front-loaded and contains no unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one parameter, no nested objects) and an output schema exists, so the return value is already structured. The description covers the key parameter and the main behavior, but it omits potential prerequisites like chat existence or owner permissions, leaving a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides zero description coverage (0%), but the description explicitly defines the only parameter, chat_id, as 'The unique identifier of the chat to share.' This fully compensates for the lack of schema detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action: 'Enable public sharing for a Synthesis Studio chat' and the outcome: 'Returns a shareable link.' It uses a specific verb and resource, and it distinguishes itself from sibling tools (e.g., get_synthesis_chat, delete_synthesis_chat) by focusing on sharing functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by explaining that anyone can access the link without authentication, which signals when to use it (when public access is desired). However, it does not explicitly state when not to use it or mention alternatives, though no sibling tool performs sharing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses prerequisites ('Analytics must be generated first') and return contents ('transcript highlights, task performance, key quotes, and AI-generated observations'). It does not mention failure modes, auth requirements, or side effects, but for a read-only report retrieval, the disclosed context is strong.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured, front-loaded with purpose, and uses concise paragraphs. The prerequisite is clearly flagged, the return value is summarized, and the args are listed cleanly. 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.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description adequately covers prerequisites, usage context, and high-level return content. It also mentions the required ID retrieval flow. For a tool with three parameters and clear dependencies, this is a complete and self-sufficient description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. However, the 'Args' section only repeats 'The unique identifier of...' for each parameter, adding no real semantic value beyond the parameter names. The hint to call get_participants first does provide some practical ID guidance, but it is insufficient for low coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Get the AI analysis report for a single participant's session,' which is a specific verb and resource with a clear singular scope. It differentiates from siblings like get_sessions_report and get_participant_sessions_report by emphasizing 'single participant's session.'
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
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 this when investigating one specific participant's performance, responses, or behavior in detail.' It also provides important prerequisites (generate_analytics, poll get_analytics_status, call get_participants), but does not explicitly name alternatives or when-not-to-use scenarios, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It prominently warns 'DESTRUCTIVE,' states that the full conversation history will be permanently lost, and explains the confirmation step to protect against accidental deletion. It doesn't mention return values or permissions, but given the simplicity and output schema, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description opens with a clear warning, states the action, then provides a direct workflow instruction and a parameter line. There are no unnecessary words; the quoted confirmation prompt is a useful concrete detail. The structure front-loads the most critical information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is a simple single-parameter delete operation. The description covers the destructive nature, the required two-step confirmation process, and the parameter identifier. Given that an output schema exists, returning value details aren't needed. The description is fully sufficient for an agent to safely invoke this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema only defines chat_id as a required string with no further description. The description adds 'The unique identifier of the chat to delete,' which clarifies the parameter's role but doesn't provide additional formatting or source details. With 0% schema coverage, the description only partially compensates; the one-line summary is minimal but adequate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool deletes a Synthesis Studio chat and its entire message history, using the specific verb 'delete' and identifying the resource. This distinguishes it from sibling tools like create_synthesis_chat, get_synthesis_chat, and send_synthesis_message.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly instructs not to call the tool in the same turn as the deletion request and to first ask for user confirmation, then only call after a follow-up confirmation. While it doesn't name alternative tools, the when/when-not guidance is explicit and actionable for a destructive operation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 discloses that the tool returns an AI-generated text summary (typically 200–400 words) and highlights a critical prerequisite: analytics must be generated first, with steps to verify or generate. This adds meaningful context beyond the basic read operation, though it could also mention potential error behaviors or side effects if analytics are unavailable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear opening sentence, bullet-pointed alternatives, a highlighted prerequisite, return value, and argument listing. It is slightly longer than strictly necessary but every sentence serves a purpose, and the front-loading of the main use case is effective. The explicit 'Args:' section is somewhat redundant with the schema but helpful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (single parameter, output schema exists), the description is fully complete for an agent. It covers the return value (short summary text), the prerequisite workflow, when to use it, and how to select deeper sibling tools. There are no significant gaps in information needed for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has zero description coverage, so the description must compensate. It provides a minimal definition: 'study_id: The unique identifier of the study.' While this is sufficient for a single self-explanatory parameter, it adds little beyond the parameter name itself. It does not include examples, format hints, or how to obtain the ID, which would have raised the score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('a short, concise AI-generated text summary of the study's overall findings'). It also distinguishes itself from sibling tools by explicitly listing follow-up tools for deeper analysis, such as get_qualitative_insights and get_quantitative_data. This goes beyond a generic purpose and fully differentiates from alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Use this first when a user asks for a quick overview of study results.' It names specific sibling tools for deeper dives and provides a clear prerequisite workflow (check get_analytics_status, then generate_analytics if needed). This is exemplary guidance with alternatives and exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It adds the important prerequisite that analytics must be generated first, and warns about the need to check status and generate if necessary. The read-only nature is implied by the verb 'get', and the description does not disclose side effects or authorization requirements, but the prerequisite is a valuable behavioral note.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with a clear purpose statement and uses bullet-like examples and a prominent prerequisite warning. It is a bit longer than necessary but every sentence carries information, and the Args section is minimal and helpful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a simple one-parameter tool and an output schema (not shown but present), the description effectively covers what the tool does, when to use it, relevant contexts, and the critical prerequisite. It does not need to explain return values due to the output schema, and it provides enough guidance for an 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.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must explain the parameter. It does so with 'study_id: The unique identifier of the study,' which adds meaningful context (uniqueness) beyond the schema's type/title. For a single required parameter, this is sufficient, though not elaborate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool gets quantitative metrics for a study, naming specific examples like task completion rates, times, and misclicks. It distinguishes itself from sibling tools such as get_qualitative_insights by explicitly focusing on numbers, and from get_analytics_status by describing the actual data retrieval rather than status checking.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides explicit usage scenarios ("Use this when the user wants numbers") and gives concrete example queries. It also mentions the most relevant sections (prototype and conceptTesting) and a clear prerequisite workflow with get_analytics_status and generate_analytics, though it does not explicitly say when NOT to use it relative to other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It adds some context by specifying 'configuration and settings,' but does not explicitly state read-only nature, error behavior, or permissions. The verb 'Get' implies safety, so this is adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and well-structured: purpose, usage hint, then args. No fluff or redundant information; every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-object retrieval with an output schema, the description covers the purpose, prerequisite, and parameter meanings. It lacks explicit error handling or edge-case details, but the tool's simplicity and output schema reduce the need for more.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema descriptions are absent (0% coverage), so the description compensates by providing clear definitions for both parameters. 'section_id' is further clarified as 'section/task,' adding useful meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get') and a specific resource ('specific section') with clear scope ('full details including its configuration and settings'). It distinguishes from sibling get_sections by focusing on a single section and even references get_sections for ID lookup.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use an alternative: 'Call get_sections first if you need to find the section_id.' This gives clear guidance on the prerequisite and differentiates from the list operation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given no annotations, the description carries the full disclosure burden. It clarifies the tool returns raw, unprocessed interaction data and contrasts it with an analyzed report, which implies read-only behavior. It does not describe potential limitations or errors, but the data type is clearly conveyed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the main purpose and followed by succinct use-case and alternative. The Args section is slightly repetitive ('unique identifier of the...') but the overall structure is clean and every line contributes value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema provided, the description does not need to detail return values. It covers the three-parameter dependency, the contrast with the report tool, and the recommended call to get_participants first, making this complete for a getter tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% (no descriptions in input schema), so the description is the only source of parameter meaning. The Args list labels each ID as 'the unique identifier of the study/participant/session', which is helpful but minimal. The note to call get_participants first adds practical context for obtaining these IDs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb-resource combination: 'Get raw transcript and interaction events for a single participant session.' It clearly distinguishes itself from sibling tools like get_session_report by explicitly mentioning the AI-analyzed summary alternative.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use guidance: 'Use this when the user wants to read what a participant actually said verbatim, or review the full unprocessed interaction log.' It also names the alternative tool (get_session_report) and gives ordering advice with get_participants.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full disclosure responsibility. It transparently warns that the operation takes 30-90 seconds, instructs the agent to set user expectations, and describes the automatic generation steps and the requirement to present a study summary afterward. It does not mention error handling or permissions, but for a creation action these are less critical.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but well-structured with headings, bullet lists, and a front-loaded latency warning. Each section earns its place, though there is some redundancy between the 'For best results' bullet list and the Args explanation, so it could be slightly tighter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only one parameter but complex generative behavior, the description covers input requirements, the generation process, latency, and the agent's post-creation action. Since an output schema exists, return values need not be described here. This is complete enough for an agent to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the parameter is just a generic string, but the description compensates thoroughly. It explains the 'description' parameter should be a natural language description including product, target users, research goals, tasks, and prototype URLs. This gives the agent concrete guidance on what to collect from the user, far exceeding the schema's bare type definition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a clear verb+resource ('Create a new UX research study using AI-powered generation') and reinforces this by stating it is the PRIMARY way to create studies. This distinguishes it from sibling tools such as get_study, update_study_name, and copy_study, which manage or transform existing studies.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'This is the PRIMARY way to create studies' and provides detailed guidance on what to include in the description for best results. However, it does not explicitly list alternatives or when not to use the tool, though the creation context is clear relative to the read/update/delete siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It explains that Synthesis Studio conducts AI-powered conversations analyzing actual session data, and clarifies that analytics need not be pre-generated. This adds behavioral context beyond a simple 'create' action, though it does not discuss side effects, permissions, or potential errors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the main purpose, followed by short context paragraphs and a clear Args list. Every sentence adds value without unnecessary fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with only 2 flat parameters and has an output schema, so the description need not detail return values. It covers purpose, usage timing, prerequisites, and parameter semantics. It could mention potential errors or whether the chat is unique per study, but overall it is coherent and sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides only titles and types, while the description's Args section adds explicit meaning: study_id is 'the unique identifier of the study to analyze' and title is 'an optional title for the chat session.' This fully compensates for the 0% schema description coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Start a new Synthesis Studio AI analysis chat for a study', which specifies the verb (start) and resource (chat). It is distinct from sibling tools like delete_synthesis_chat, send_synthesis_message, and list_synthesis_chats.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says 'Best used after the study has collected participant sessions' and notes that 'Analytics do not need to be pre-generated', providing clear when-to-use context. It also directs users to 'use send_synthesis_message to ask questions' after creation, which points to the next step. However, it does not explicitly list alternative tools or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses the 2-5 minute generation time, the concept of 'stale' components, and implies a non-destructive status check. It does not explicitly state whether it is read-only, but the context makes this clear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with a clear purpose statement, actionable decision bullets, and a short args section. Every sentence earns its place, with no redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter status-check tool with an output schema, the description provides a complete decision workflow, including when to use it, what it returns, and how to proceed. It covers all necessary context for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only one parameter, study_id, is described as 'the unique identifier of the study'. This adds minimal meaning beyond the schema field name. Since schema description coverage is 0%, the description partially compensates, but the parameter is simple and self-explanatory.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Check' and resource 'what analytics have been generated for a study', and lists the components (qual, quant, QnA, summary, insights, report). It clearly distinguishes itself from sibling read tools by being a status gate before generation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit usage guidance is given: if all needed components are present, call read tools directly; if missing or stale, call generate_analytics first (with time estimate). It also instructs to always call this before generate_analytics to avoid unnecessary work.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It explains the process (fetch, extract, return), implies read-only analysis, and adds important caveats: user_id must be the account owner's UID and is NOT the study_id. This goes beyond the schema and provides meaningful behavioral context, though it does not explicitly state safety traits or potential side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with a clear one-sentence purpose, followed by a concise process statement and then detailed but relevant parameter explanations. Every sentence earns its place; the note about user_id is essential and not redundant.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has two parameters, no annotations, and an output schema. The description covers the core behavior, input semantics, and a key caveat. Given the output schema exists, detailed return values are not needed in the description. It is contextually complete, though it could mention any access prerequisites or error conditions, but the existing content is sufficient for an agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate—and it does excellently. Both user_id and figma_link are thoroughly explained, including the critical distinction that user_id is the account owner's UID, not the study_id, and advice to ask the user if unknown. This adds substantial meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Analyze a Figma prototype and extract screen context for UX research,' providing a specific verb (analyze) and resource (Figma prototype). It further clarifies that it fetches the file, extracts screen information, and returns structured data about pages and frames, which clearly distinguishes this from the sibling study/session management tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for usage: it is for UX research on Figma prototypes, as stated in the first sentence and the process description. It does not explicitly mention alternatives or when not to use the tool, but the context is clear enough for an agent to select it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the return behavior ('full guide content (markdown)') and the filtering rule ('for each section that has one'), which is sufficient for a simple read-only getter. It does not mention error cases, but that is a minor gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded: a clear one-sentence purpose, a short return clarification, a usage hint, and an args block. Every sentence earns its place with no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one parameter and an output schema. The description covers purpose, return content, and timing. It does not need to explain output schema details since the output schema exists, and the usage guidance is sufficient for tool selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates by explaining study_id as 'The unique identifier of the study.' This adds meaning beyond the schema's type/title, and with only one parameter, this is adequate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description begins with a specific verb and resource: 'Get all interview guides for every section in a study.' It clearly identifies the scope (all sections), the object (interview guides), and differentiates from siblings like get_sections or generate_guide by mentioning the full guide content in markdown.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear usage context: 'Use this after study creation to see the AI-generated interview guides.' This tells the agent when to invoke the tool, though it does not explicitly state when not to use it or name alternative tools for exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the return fields (ID, name, session IDs, completion status) and implies read-only behavior through 'List' and 'Returns.' It lacks explicit note on side effects, pagination, or error handling, but the provided detail is meaningful and goes beyond the tool name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concisely structured into three clear sections: purpose, usage guidance, and parameters. Every sentence adds value, and the most important usage signal ('Call this first') is front-loaded. No filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with an output schema, the description provides complete context: what it returns, who depends on it, and what the argument means. It covers all necessary information without over-explaining, fully fitting the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no parameter description (coverage 0%), but the tool description includes an Args section explaining 'study_id: The unique identifier of the study.' This adds needed semantic meaning for the single parameter, though it is somewhat generic and could mention format or example.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description begins with 'List all participants in a study' using a specific verb and resource, and adds context about session status and metadata. This clearly distinguishes it from sibling tools like get_session or get_sessions_report, which focus on sessions rather than the participant roster.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states 'Call this first whenever you need participant_id or session_id values' and lists dependent tools, giving strong when-to-use guidance. It does not provide explicit when-not-to-use or alternative tools, but the clear prerequisite covers most needs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the important prerequisite that analytics must be generated first, including a fallback action, and it states what the response includes (overview, positive findings, challenges). It does not detail error handling or edge cases, but it provides meaningful behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clearly labeled sections for purpose, usage, prerequisite, and arguments. It is slightly verbose but every sentence adds value, and the main purpose is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with an existing output schema, the description covers the purpose, usage scenario, prerequisite workflow, and contents of the response. The output schema handles the return structure, so no additional detail is needed for an agent to select and invoke this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates by defining study_id as 'the unique identifier of the study' in plain language. For a single parameter, this provides complete and unambiguous semantics beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves qualitative analysis for a study, specifying themes, patterns, and insights. It uses a specific verb ('Get') and resource, and the emphasis on the 'why' behind participant behavior distinguishes it from quantitative and summary sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says when to use this tool (when understanding 'why' behind participant behavior) and provides a prerequisite flow involving get_analytics_status and generate_analytics. It does not mention alternatives or situations when not to use it, so it falls just short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. 'List all sections' clearly indicates a read-only operation, and it discloses the output fields (types, names, IDs). It does not discuss pagination, authentication, or edge cases, but for a simple list tool the behavioral disclosure 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief, front-loaded with the action, and every sentence serves a purpose. The Args section is redundant with the schema but harmless. No fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter list tool with an output schema, the description is complete. It states what the tool returns, how to use it, and why it is useful. It does not need to explain return values because an output schema exists.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides no parameter description (0% coverage), but the description adds 'study_id: The unique identifier of the study.' This is minimal and does not specify format, source, or examples. It adds some meaning beyond the schema but not enough to fully compensate for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'List all sections (tasks) in a study with their types, names, and IDs,' using a specific verb and resource. It clearly distinguishes itself from siblings like get_section (single section) and create/update/delete operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly tells the agent when to use this tool: 'Use this to find section_id values needed for get_section, update_section, delete_section, generate_guide, and update_interview_guide.' This names the dependent tools and frames the tool as an ID lookup, which is strong usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses a key behavioral trait: analytics must be generated first, and directs the user to get_analytics_status if components are missing. The 'Get' verb implies a read operation, though it doesn't explicitly state that no mutation occurs. This is more transparent than typical descriptions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the purpose, then usage triggers, alternatives, and a prerequisite warning. Every sentence earns its place; no fluff or repetition exists.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter and an output schema, the description covers the purpose, when to use, alternatives, and necessary prerequisites. The dependency on generated analytics is clearly explained, making the tool's context fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has only one parameter, study_id, with no property description (0% schema coverage). The description's Args section provides a minimal explanation: 'The unique identifier of the study.' This adds only slightly more meaning than the parameter name itself, so it barely compensates for the missing schema-level description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Get overall and per-section insights for a study, including key findings and actionable recommendations.' This is a specific verb+resource combination that distinguishes it from siblings like get_analytics_summary and get_study_report.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells when to use the tool: when the user asks 'what should we fix', 'what are the main issues', or wants prioritized action items broken down by section. It also names alternatives: get_analytics_summary for a shorter executive summary, and generate_study_report + get_study_report for a full combined report. The prerequisite workflow with get_analytics_status and generate_analytics adds clear usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 reveals that analytics must be generated first and that data may be sparse or empty for certain study types, which is important for setting expectations. However, it does not mention potential errors or rate limits, but it does provide meaningful behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded: it begins with a clear purpose, then provides context, a prerequisite warning, and an args section. Every sentence adds value, and the use of a warning emoji for the prerequisite draws attention. It is appropriately sized with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool (one parameter, output schema present), the description covers all necessary aspects: purpose, usage context, prerequisite, and data variability. The output schema documents return values, so the description does not need to explain them. It is complete for an agent to understand when and how to invoke this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides a basic explanation for study_id as 'The unique identifier of the study.' While this adds minimal meaning beyond the parameter name, the parameter is simple, and the description does not explain how to locate or format the ID. This is adequate but not rich.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with a clear verb+resource: 'Get UI annotation data (heatmaps, click data) for a study.' It further specifies that it is most relevant for prototype or conceptTesting sections and mentions that data may be sparse for voice interviews, which distinguishes it from other study data tools like get_quantitative_data or get_qualitative_insights.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states when the tool is most relevant (prototype/conceptTesting sections) and when it may be less useful (voice interview sections). It also provides a clear prerequisite workflow: check get_analytics_status and call generate_analytics if components are missing. This gives concrete guidance on when to use the tool and what to do before invoking it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description discloses key behavioral traits: it warns that responses take 30–90 seconds and advises to warn the user, and it explicitly outlines the return value ('AI analyst's response text with findings, patterns, or answers'). This adds practical context beyond a simple function signature. It doesn't mention side effects like whether the message is saved to chat history, but the latency and return type are significant disclosures, so a 4 is warranted.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately concise. It opens with a clear purpose, gives usage guidance, adds a critical latency note with a user-facing warning, states the return value, and lists parameters. Every sentence serves a purpose without unnecessary repetition or filler, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 params, no nested objects) and the presence of an output schema, the description is nearly complete. It covers the tool's purpose, when to use it, the expected latency, the return value, and the prerequisite relationship to create_synthesis_chat. It could additionally mention behavior on invalid chat_id or whether the message is persisted, but these are minor gaps, so a 4 is appropriate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no descriptions for its parameters (coverage 0%). The description compensates fully with an explicit Args section explaining chat_id as 'The unique identifier of the chat (from create_synthesis_chat)' and message as 'The question or request to send to the AI analyst.' This goes far beyond the bare schema titles 'Chat Id' and 'Message,' giving the agent clear guidance on what each parameter means and where chat_id comes from.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Send a message to a Synthesis Studio AI chat' with the specific verb 'send' and resource 'message to chat,' and contrasts with siblings like create_synthesis_chat (creating a chat) and get_synthesis_chat (retrieving chat). It also lists concrete use cases (ask questions, explore themes, compare participants, generate insights), making the tool's purpose unambiguous and distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context: 'Use this to ask questions about research data, explore themes, request comparisons across participants, or generate new insights from the data.' It also implies a prerequisite by specifying chat_id comes from create_synthesis_chat. However, it does not explicitly mention when not to use it or name alternative tools for similar actions, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well, disclosing key behavioral traits: 'The copy starts in Draft status with no participant sessions' and enumerating exactly what is copied. It could add more details about permissions or non-copied fields, but the core behavior is transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the main action first. Every sentence adds value, and the Args section is directly tied to the parameter, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter and no annotations, the description covers the operation's behavior, the resulting state, and an explicit use case. The presence of an output schema means return values are already handled, so the description is complete for this scope.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description fully compensates for 0% schema coverage by explaining the only parameter: 'study_id: The unique identifier of the study to copy.' This gives the agent precise semantic meaning beyond the bare property title.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action: 'Duplicate an existing study and all its sections.' It uses a specific verb and resource, and it distinguishes itself from siblings like create_study and delete_study by describing the copy behavior and scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear use case: 'Useful for iterating on a study template without modifying the original.' This implies when to use the tool, but it does not explicitly name alternatives or exclusion criteria, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses important behavioral details: the created section is incomplete until update_section is called, and generate_guide must follow. It also notes the return object. This goes beyond a simple 'creates a section' statement, though it does not cover error conditions or prerequisites like a valid study_id.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections: purpose, required steps, section types, return value, and parameters. Every sentence contributes useful information, with no filler or repetition. The formatting makes it easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simple create operation, the description covers all essential aspects: what it does, required follow-up actions, valid section types, parameter semantics, and the return object. It is complete for an agent to invoke the tool and handle the result appropriately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema description coverage, the description fully explains all three parameters: study_id, section_type with explicit allowed values, and index with its default. This compensates entirely for the lack of schema documentation and adds contextual meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb+resource: 'Add a new section to a study.' It clearly differentiates from sibling tools like get_sections, update_section, and delete_section by indicating this is the creation operation. The section type enumeration further clarifies the scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'REQUIRED NEXT STEPS' section explicitly states that after creation, update_section and generate_guide must be called, giving the agent a clear workflow context. However, it does not explicitly mention when not to use this tool or name alternative tools for non-creation scenarios, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the return behavior ('Aggregated report object with overall findings, patterns, and per-section summaries across all participants') and emphasizes scope ('ALL sessions'). It does not mention side effects or access requirements, but for a read-only report tool 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with separate sections for purpose, usage, returns, and args. It is slightly longer than necessary but every sentence contributes to guidance or clarification. The front-loaded purpose is immediately clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only one parameter and an output schema, the description covers all essential aspects: what it does, when to use it, how it differs from related tools, what it returns, and what the parameter means. No significant gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage (only a title 'Study Id'). The description compensates by explicitly listing the argument: 'Args: study_id: The unique identifier of the study.' Though minimal, it adds enough meaning for a simple single parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb+resource: 'Get an aggregated analysis report across ALL sessions in a study.' It clearly distinguishes this from sibling tools by explicitly naming alternatives for single-session and participant-level reports.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
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 ('Use this for study-level synthesis') and provides direct alternatives with exact function signatures: 'For a single participant's session, use get_session_report(study_id, participant_id, session_id). For all sessions by one participant, use get_participant_sessions_report(study_id, participant_id).' This is clear when/when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden. It clarifies that the full guide content in markdown is replaced, and the workflow constraint ('generate first') is disclosed. However, it does not mention error conditions, permissions, or side effects, which would further enrich transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with a clear one-sentence purpose, a workflow note, and a compact args list. All sentences earn their place without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, return values need not be explained. The description covers purpose, usage, and parameters but omits error handling or prerequisites beyond the generation workflow, which is a minor gap for a mutation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description compensates by documenting each parameter in the Args section. It clarifies study_id and section_id as identifiers and notes guide_content is 'the full guide content in markdown format', adding meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Replace the interview guide content for a specific section' with a clear verb and resource. It distinguishes from sibling tools like generate_guide (creation) and get_interview_guides (reading) by specifying the update/replace operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly instructs users to FIRST generate a guide using generate_guide and then edit it with this tool, and warns against writing guides from scratch. This gives clear when-to-use and when-not-to-use guidance, naming the alternative tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavioral traits. It clearly states the destructive and irreversible nature ('DESTRUCTIVE', 'cannot be undone') and the cascading deletion of associated configuration. This is exemplary transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a warning label, clear explanation, explicit confirmation instruction, and an Args section. Every sentence contributes essential information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter destructive tool with an output schema, the description covers the action scope, irreversibility, and the required confirmation flow. No critical missing context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides one-line meanings for both parameters, including clarifying section_id as 'section/task'. While basic, it adds meaningful context beyond the bare parameter names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action: 'Permanently remove a section from a study.' It specifies the resource (section) and the full scope (section, interview guide, and associated configuration), which distinguishes it from sibling tools like delete_study.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit usage guidance by instructing the agent not to call the tool in the same turn as the deletion request and to wait for user confirmation. However, it does not mention alternative tools (e.g., update_section) or situations where deletion is inappropriate, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the call returns immediately, runs in the background, takes 2-5 minutes, and requires a completed session prerequisite. It also clarifies the non-blocking nature and the polling workflow, which is essential behavioral context for an async generation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but every section earns its place: purpose, prerequisite, timing, async behavior, polling instructions, and argument list. The use of emoji and bold headers (⚠️ PREREQUISITE, ⏱️) improves scannability, and the information is front-loaded with the core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex async generation tool with prerequisites and a polling loop, the description covers all necessary operational details: what to check first, how long to wait, how to check status, what to tell the user, and what to do after completion. An output schema exists, so return-value details are not required in the description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter (study_id) with 0% description coverage. The description's 'Args' line says 'study_id: The unique identifier of the study', which adds a label but is nearly tautological. It does not provide example values, format hints, or lookup guidance, so it only minimally compensates for the missing schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Trigger AI-powered analytics generation for a study', a specific verb+resource statement. It further enumerates all generated components (qualitative insights, quantitative data, Q&A analysis, summary, overall insights), clearly distinguishing this generation tool from sibling read tools like get_qualitative_insights and get_analytics_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit usage instructions: call get_participants first to verify completed sessions, call get_analytics_status first to avoid redundant generation, and after triggering, poll get_analytics_status up to 10 times with a provided fallback message. It also points to 'appropriate read tools' once generation completes, effectively contrasting with alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden. It discloses the prerequisite that analytics must already be generated, which is a critical behavioral constraint not visible from the schema. It also implies a read-only operation and describes the output organization. However, it does not discuss failure modes or authorization requirements, leaving a small gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a clear purpose sentence, a usage sentence, a prerequisite warning, and the argument definition. No filler or repeated schema content, and all critical information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter read tool with an output schema, the description covers purpose, usage, prerequisites, and parameter semantics. The output schema handles return value details, so nothing essential is missing. It also differentiates from sibling analytics tools through the 'per-participant' focus.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has zero description coverage (0%), but the description defines the only parameter as 'the unique identifier of the study,' fully clarifying its meaning. This compensates for the schema gap, though the explanation is minimal and could mention format/origin.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get') and clearly defines the resource ('per-participant Q&A analysis') with structural detail ('organized by subtopic across all study sections'). This distinguishes it from sibling analytics tools like get_quantitative_data or get_analytics_summary, which focus on aggregate or other data types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states when to use: 'Use this when the user wants to compare how different participants answered the same questions, or to see individual-level responses side by side.' It also provides a workflow prerequisite (call get_analytics_status, then generate_analytics if needed), which is actionable guidance beyond simple use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It clearly explains the output format (formatted text showing study name, status, objectives, target persona, and sections) and implies a read-only operation. It does not mention edge cases or errors, but for a simple retrieval tool this is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear opening statement, usage context, return-value breakdown, and an Args section. It is concise (about 50 words) with no filler, each line adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has a simple interface (one parameter) and provides a clear return description. The description covers purpose, usage boundaries, and expected output, making it complete for the given complexity. The presence of an output schema also means detailed return values need not be fully specified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema only has 'study_id' with the title 'Study Id' and no detailed description. The description adds meaning by explaining it as 'The unique identifier of the study,' which goes beyond the schema. With only one required parameter, this level of explanation is adequate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states 'Get a formatted, human-readable summary of a study' with a specific verb and resource. It further distinguishes itself from the sibling tool get_study by clarifying that it is for presentation, not programmatic field access.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage guidance: use it to present study details after creation or modification, and explicitly says not to use it for programmatic access, directing to get_study instead. This explicitly names an alternative and clarifies when this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the transparency burden. It discloses the return content ('IDs, names, and statuses') and indicates a read/list operation without claiming any side effects. It does not mention pagination or authentication, but for a zero-parameter list tool this is adequate and not misleading.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the core purpose and immediately followed by practical guidance. Every sentence adds value: what it lists, when to use it, and what to do next. No fluff or redundant repetition of the schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter list tool with an output schema present, the description fully covers the purpose, output contents, and recommended follow-up workflow. It is complete enough for an agent to select and invoke this tool correctly without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and an empty schema, so the baseline is 4 per the rubric. The description adds no parameter-specific meaning (there are none), but it does clarify the scope ('current tenant/organization'), which is context beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') and resource ('all studies for the current tenant/organization'), clearly distinguishing it from sibling tools. It also positions itself as the entry point ('Start here when no study_id is provided'), which makes its purpose unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
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 ('Start here when no study_id is provided') and names alternatives for follow-up actions: 'call get_study_summary for a human-readable overview or get_study for the full raw config.' This provides clear usage context and direct alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing behavior. It specifies the return fields (chat IDs, titles, associated study IDs) and the scope (all studies). It doesn't explicitly state that the operation is read-only, but 'List' strongly implies it. No pagination or filtering details are mentioned, but the description is otherwise clear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: the first fronts the core purpose, the second adds return details and usage context. Every sentence earns its place; no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a list tool with no parameters and an output schema present, the description is complete. It covers scope, return values, and use cases. The presence of an output schema means detailed return structure is not needed in the description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters, so the schema coverage is 100% (vacuous). The baseline for 0 params is 4, and there is no parameter information to add beyond the schema. The description doesn't need to explain parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists all Synthesis Studio AI chats across all studies, with a specific verb, resource, and scope. This distinguishes it from sibling tools like get_synthesis_chat (single chat), create_synthesis_chat, and delete_synthesis_chat.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use the tool: to find existing chats to resume with get_synthesis_chat, or to show the user analysis history before creating a new chat. This provides clear context and names the relevant alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full transparency burden. It discloses key behavioral traits: the requirement to generate analytics first for best results, and the dependency on get_participants to obtain the correct participant_id. The verb 'Get' implies read-only, and the focus on aggregated reporting communicates expected output. However, it doesn't explicitly state that the operation is non-mutating or mention how stale analytics might affect the report, which would have pushed this to a 5.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded: a one-sentence summary, followed by usage rationale, sibling alternatives, a clear prerequisite callout, and an Args section. Every sentence earns its place; there is no filler or redundant information. The use of a warning emoji and logical paragraph breaks improves scannability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, two simple parameters, and the presence of an output schema, the description is remarkably complete. It covers the tool's purpose, usage context, alternatives, prerequisites, and parameter semantics. The mention of analytics generation and participant lookup fills the potential contextual gaps that an agent would need to invoke it successfully.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description fully compensates by defining each parameter: 'study_id: The unique identifier of the study' and 'participant_id: The unique identifier of the participant.' It also adds practical guidance by advising to call get_participants first to find the correct participant_id, which is highly useful for an agent invoking the tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a clear, specific statement: 'Get an aggregated report for all sessions completed by a specific participant.' This uses a specific verb (Get), a clear resource (sessions report), and a defined scope (specific participant). It also distinguishes itself from siblings by explicitly naming get_sessions_report (across ALL participants) and get_session_report (single session), making its unique purpose unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides explicit when-to-use guidance: 'Use when the user wants to understand one person's overall experience or performance across multiple sessions in the same study.' It additionally states when NOT to use it by directing users to alternative tools for other needs. The prerequisite note (generate_analytics, poll get_analytics_status, call get_participants) gives clear operational context for correct invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the return structure (full study object with key fields) and contrasts with the summary tool, adding behavioral context. However, it doesn't explicitly state side effects (e.g., read-only nature) or error behavior, though the verb 'Fetch' implies safety. Minor gap, hence 4.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is succinct and well-organized: purpose statement, usage context, return summary, and args. Every sentence adds value, with no redundancy. It is front-loaded with the core action and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the description still lists key returned fields, giving the agent a quick mental model. It covers purpose, usage, return, and parameter semantics. No significant gaps are evident for a simple one-parameter fetch tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description fully compensates by providing an 'Args' section: 'study_id: The unique identifier of the study.' This unambiguously defines the only parameter, exceeding the schema's bare string type.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches the full study configuration including sections, status, objectives, persona, product overview, and metadata. It also explicitly differentiates from the sibling get_study_summary by contrasting raw data vs. human-readable overview.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use guidance: 'Use this when you need raw study data for programmatic processing or to check specific field values.' It also names the alternative: 'For a human-readable overview to present to the user, prefer get_study_summary.' This is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses destructive, permanent behavior, lists affected data (sessions, recordings, transcripts, analytics, interview guides), and states it cannot be undone. This goes well beyond the minimum and eliminates ambiguity about consequences.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description starts with a prominent DESTRUCTIVE warning, then provides essential details in a clear hierarchy: what it deletes, irreversibility, confirmation protocol, and parameter explanation. Every sentence carries important information, with no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive tool with one parameter and no annotations, the description covers all critical aspects: the action, the permanent consequences, the exact data affected, the required confirmation workflow, and the parameter meaning. An output schema exists, so return values don't need explanation. No gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema only provides the parameter name 'study_id' with type string and no description. The description's Args section adds semantic meaning by identifying it as 'the unique identifier of the study to delete', clarifying its purpose. This is helpful, though minimal for a single simple parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'delete' with the resource 'study' and clearly states it permanently deletes the study and ALL associated data. It distinguishes from sibling tools like delete_synthesis_chat and delete_section by focusing on the study resource and its full data scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use instructions: do NOT call in the same turn as the deletion request, instead first respond with a confirmation message, and only call after user confirms in a follow-up. This is clear, actionable guidance that also prevents misuse.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses the 30–90 second latency and directs warning the user. Details the post-generation workflow (present guide, use update_interview_guide for changes) and return format (markdown). With no annotations, this fully describes expected behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear sections: purpose, prerequisites, latency, post-generation guidance, return, and args. All sentences add value; front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers prerequisites, runtime behavior, follow-up steps, and return format. With only two simple parameters and a straightforward outcome, this is adequate for an agent to execute correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has no descriptions and coverage is 0%, so description compensates by defining study_id and section_id as unique identifiers. Adds an Args section with plain-language definitions, though it doesn't provide examples or where to locate these IDs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it generates an AI-powered interview guide for a section, using study objectives, persona, and section config. Distinguishes from update_interview_guide and get_interview_guides by its generation-specific action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly lists prerequisites (objectives, persona, section name/description) and directs users to update_study_details and update_section if unmet. Also instructs to use update_interview_guide for changes instead of regenerating, providing clear when-to-use vs alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that generation takes 3-5 minutes, the call returns immediately, processing runs in the background, and gives exact polling behavior (up to 10 checks) and user messaging. This is exemplary transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with clear prerequisites, timing, polling instructions, and next steps. Every sentence provides essential operational detail; no redundancy or filler. The length is justified by the complexity of the asynchronous workflow.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (async background generation, dependencies, polling), the description covers all necessary context: prerequisites, expected duration, immediate return behavior, polling policy, failure fallback, and handoff to retrieval. An output schema exists, so not explaining return values is acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides only the parameter name and type (string) with no description (0% coverage). The description fills this gap by stating 'The unique identifier of the study,' adding clarity beyond the schema. While minimal, it properly compensates for the otherwise undocumented parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description begins with 'Trigger generation of the comprehensive study report,' using a specific verb and resource. It clearly distinguishes from the sibling tool get_study_report (which retrieves the report) and generate_analytics (a prerequisite step).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states prerequisites (analytics must be generated first), tells the agent to call get_analytics_status to check, provides conditional instruction to call generate_analytics if missing, and specifies to poll get_analytics_status and then call get_study_report. This is comprehensive when-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully carries the behavioral disclosure burden. It warns that an empty or 'not found' response means the report hasn't been generated, gives a step-by-step recovery workflow, and describes the return format (markdown/structured with qualitative insights, quantitative data, Q&A analysis, recommendations).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with a clear purpose, followed by necessary workflow details and recovery steps. Every sentence adds value, and the structure moves from summary to prerequisites to recovery to return format, making it easy for an agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (dependency on prior generation, possible missing states, and multi-step recovery), the description is complete. It covers prerequisites, verification, failure handling, return content, and the single parameter, making it sufficient even without annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides no description for study_id (0% coverage), but the description explicitly states 'Args: study_id: The unique identifier of the study.' This adds basic semantic meaning beyond the bare schema, though it is minimal and mostly aligns with the parameter name.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
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: 'Retrieve the full comprehensive study report combining all analytics.' This clearly distinguishes it from siblings like get_study_summary, get_qualitative_insights, and get_analytics_status, which serve more granular or alternative retrieval purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states the prerequisite: 'This report must be generated first using generate_study_report.' It also instructs the agent to verify readiness via get_analytics_status and provides a detailed recovery path with alternative tool calls, making when-to-use and recovery behavior unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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