Fatebook MCP Server

by an1lam

Overview Schema Related Servers Score Discussions

Python

Remote

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose targeting specific actions on Fatebook questions or related entities. There is no overlap in functionality, with tools for creating, editing, deleting, getting, listing, resolving questions, and adding comments or forecasts, all well-separated.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case, such as create_question, add_comment, and resolve_question. This uniformity makes the tool set predictable and easy to understand for an agent.
Tool Count5/5
With 9 tools, the server is well-scoped for managing Fatebook questions, covering core operations like CRUD, listing, resolving, and adding forecasts/comments. Each tool serves a clear purpose without redundancy, fitting the domain appropriately.
Completeness5/5
The tool set provides complete coverage for the Fatebook question domain, including full CRUD operations (create, get, edit, delete), listing with filtering, resolution, and auxiliary actions like adding comments and forecasts. No obvious gaps exist for typical agent workflows.
Average 2.8/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 provided, the description carries the full burden of behavioral disclosure. It states 'Create' which implies a write/mutation operation, but fails to mention permissions required, rate limits, side effects (e.g., how sharing options affect visibility), or what the output schema returns. This leaves significant gaps in understanding the tool's 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?
The description is a single, clear sentence with no wasted words, making it highly concise and front-loaded. It directly states the tool's purpose without unnecessary elaboration, which is efficient for an AI agent.
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?
Given the complexity of 9 parameters (3 required), 0% schema coverage, no annotations, and the presence of an output schema, the description is incomplete. It doesn't explain parameter meanings, usage context, or behavioral traits, relying too heavily on the output schema to cover return values while leaving other critical aspects unaddressed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning none of the 9 parameters have descriptions in the schema. The tool description adds no information about parameters like 'resolveBy', 'forecast', or 'sharePublicly', failing to compensate for the lack of schema documentation. This leaves all parameters semantically undefined.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Create a new Fatebook question' clearly states the action (create) and resource (Fatebook question), but it's vague about what a 'Fatebook question' entails and doesn't differentiate from sibling tools like 'edit_question' or 'resolve_question'. It specifies the resource type but lacks detail on the nature of the creation.
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 provided on when to use this tool versus alternatives such as 'edit_question' for updates or 'list_questions' for viewing. The description implies usage for creation but offers no context on prerequisites, timing, or exclusions, leaving the agent to infer based on tool names alone.
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 of behavioral disclosure. 'Edit' implies a mutation operation, but the description doesn't disclose any behavioral traits such as required permissions, whether edits are reversible, rate limits, or what the output looks like. This leaves significant gaps in understanding how the tool behaves beyond its basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single sentence, 'Edit a Fatebook question', which is front-loaded and wastes no words. While it may be under-specified, it earns full marks for brevity and clarity within its limited scope.
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?
Given the complexity (a mutation tool with 5 parameters, 0% schema coverage, no annotations, but with an output schema), the description is incomplete. It doesn't address key aspects like parameter meanings, behavioral context, or usage guidelines. The presence of an output schema reduces the need to explain return values, but other gaps remain significant for effective tool selection and invocation.
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?
The input schema has 5 parameters with 0% description coverage, so the schema provides no semantic information. The description adds no meaning beyond the tool name—it doesn't explain what 'questionId', 'title', 'resolveBy', or 'notes' represent or how they affect the edit. For a tool with multiple parameters and no schema descriptions, this is inadequate compensation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Edit a Fatebook question' clearly states the verb ('Edit') and resource ('Fatebook question'), making the basic purpose understandable. However, it doesn't differentiate from sibling tools like 'create_question' or 'delete_question' beyond the obvious verb difference, nor does it specify what aspects can be edited. This makes it vague about the scope of editing.
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. It doesn't mention prerequisites (e.g., needing an existing question), exclusions, or comparisons to siblings like 'create_question' for new questions or 'delete_question' for removal. Without such context, users must infer usage from the tool name alone.
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 states the action ('Add a forecast') which implies a write/mutation operation, but doesn't disclose any behavioral traits such as permissions needed, whether this is idempotent, what happens if a forecast already exists, rate limits, or error conditions. This is a significant gap for a mutation tool with zero annotation coverage.
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 at just 7 words, front-loading the core purpose without any wasted words. Every word earns its place by specifying the action and target resource efficiently.
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 mutation tool with 4 parameters (2 required), 0% schema description coverage, no annotations, and sibling tools that could cause confusion, the description is inadequate. While an output schema exists (which helps with return values), the description doesn't provide enough context about when to use this tool, what the parameters mean, or the behavioral implications of adding a forecast.
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%, meaning none of the 4 parameters have descriptions in the schema. The tool description doesn't mention any parameters at all, failing to compensate for the complete lack of schema documentation. The agent must infer parameter meanings from titles like 'Questionid' and 'Forecast' without any contextual guidance.
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 action ('Add a forecast') and the target resource ('to a Fatebook question'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'count_forecasts' or 'resolve_question' that also involve forecasts, leaving some ambiguity about when this specific tool should be used versus alternatives.
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 like 'count_forecasts' or 'resolve_question'. It doesn't mention prerequisites, context, or exclusions, leaving the agent to infer usage based solely on the tool name and parameters.
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 states 'Delete' which implies a destructive mutation, but doesn't specify if deletion is permanent, reversible, requires specific permissions, or has side effects (e.g., removing associated forecasts). This leaves significant gaps for a destructive 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 a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration.
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?
Given a destructive tool with no annotations, 0% schema coverage, and an output schema (which might help), the description is incomplete. It lacks critical context like behavioral traits, parameter meanings, and usage guidelines, making it inadequate for safe and effective tool invocation.
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 for undocumented parameters. It mentions no parameters at all, failing to explain what 'questionId' represents (e.g., a unique identifier) or the optional 'apiKey' (likely for authentication). This adds no value beyond the bare schema.
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 'Delete a Fatebook question' clearly states the action (delete) and the resource (Fatebook question), providing specific verb+resource. However, it doesn't differentiate from siblings like 'edit_question' or 'resolve_question' in terms of when deletion is appropriate versus other modifications.
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. It doesn't mention prerequisites (e.g., needing question ownership), exclusions (e.g., cannot delete resolved questions), or comparisons to siblings like 'edit_question' for modifications instead of deletion.
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 full burden for behavioral disclosure. It states the tool 'Returns a structured Question object with all available fields' which provides some output information, but doesn't cover important aspects like authentication requirements (apiKey parameter exists but isn't explained), rate limits, error conditions, or whether this is a read-only operation. The description is minimal and leaves significant behavioral gaps.
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 appropriately concise with two sentences that get straight to the point. The first sentence states the purpose, the second describes the return value. There's no wasted language or unnecessary elaboration. However, it could be slightly improved by front-loading more critical information about parameters.
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?
Given that an output schema exists, the description doesn't need to explain return values in detail. However, for a tool with 2 parameters (0% schema coverage), no annotations, and siblings that perform similar operations on questions, the description is incomplete. It should provide more context about when to use this versus other question tools and explain the parameters, especially the apiKey.
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 but fails to do so. The description mentions 'specific Fatebook question' which implies the 'questionId' parameter, but doesn't explain what format it expects or where to find it. It completely ignores the 'apiKey' parameter. With 2 parameters and 0% schema coverage, the description adds minimal semantic value beyond what's obvious from the tool name.
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's purpose: 'Get detailed information about a specific Fatebook question' - a specific verb ('Get') and resource ('Fatebook question'). It distinguishes from siblings like 'list_questions' (which lists multiple) by focusing on a single specific question. However, it doesn't explicitly contrast with 'edit_question' or 'resolve_question' which also operate on specific questions.
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. It doesn't mention when to use 'get_question' versus 'list_questions' (for multiple questions) or versus other question-specific tools like 'edit_question' or 'resolve_question'. There's no context about prerequisites, timing, or exclusions.
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 states the action ('Add a comment') which implies a write/mutation operation, but doesn't address permissions, whether comments are editable/deletable, rate limits, or what happens on success/failure. This leaves significant gaps for a mutation 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 a single, efficient sentence with zero wasted words. It's appropriately sized for a simple tool and front-loads the core purpose immediately.
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?
Given the tool has an output schema (which handles return values), no annotations, and moderate complexity (3 parameters with 0% schema coverage), the description is minimally adequate but incomplete. It states the basic purpose but lacks parameter details, behavioral context, and usage guidelines that would be helpful for an AI agent.
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 schema provides no parameter documentation. The description mentions 'question' and 'comment' which map to two parameters, but doesn't explain what 'questionId' represents (e.g., ID format), what 'comment' content is allowed, or the purpose of 'apiKey' (authentication). It adds minimal value beyond parameter names.
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 action ('Add a comment') and the target resource ('to a Fatebook question'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'edit_question' or 'resolve_question' that might also involve question modifications, missing full sibling distinction.
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's no mention of prerequisites (e.g., needing an existing question), exclusions, or comparisons to other tools like 'edit_question' for modifying question content versus adding comments.
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 states the action ('count forecasts') without any details on permissions, rate limits, return format, or side effects. For a tool with zero annotation coverage, this is inadequate, as it lacks critical operational 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 a single, efficient sentence with no wasted words, making it easy to parse. It's appropriately sized for a simple tool, though this conciseness comes at the cost of detail in other dimensions.
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?
Given the tool's low complexity (one parameter) and the presence of an output schema (which handles return values), the description is minimally complete. However, with no annotations and poor parameter documentation, it lacks sufficient context for safe and effective use, scoring at the baseline of adequacy with clear gaps.
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?
The input schema has 0% description coverage, so the parameter 'userId' is undocumented in the schema. The description adds minimal semantics by implying it's used to specify a user, but it doesn't explain the format (e.g., string ID), constraints, or examples. This fails to compensate for the schema gap, leaving the parameter poorly understood.
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 verb 'count' and the resource 'forecasts', specifying the scope 'for a specific user'. However, it doesn't differentiate from potential sibling tools like 'list_questions' or 'get_question' that might also involve counting or retrieving data, so it doesn't reach the highest score.
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 minimal guidance by implying usage when counting forecasts for a user, but it offers no explicit when-to-use advice, no alternatives (e.g., vs. 'list_questions' for broader data), and no prerequisites. This leaves the agent with little context for tool selection.
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 states the tool resolves questions but doesn't cover critical aspects like required permissions, whether the resolution is reversible, rate limits, or what happens upon invocation. This leaves significant gaps in understanding the tool's 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?
The description is a single, efficient sentence that front-loads the core purpose without any wasted words. It directly communicates what the tool does, making it highly concise and well-structured for quick understanding.
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?
Given the tool's complexity (a mutation with 4 parameters) and the presence of an output schema (which reduces the need to describe return values), the description is minimally adequate. However, with no annotations and low schema coverage, it lacks details on behavior and parameters, making it incomplete for safe and effective use.
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%, meaning parameters are undocumented in the schema. The description mentions 'YES/NO/AMBIGUOUS resolution,' which hints at the 'resolution' parameter's possible values but doesn't explain 'questionId,' 'questionType,' or 'apiKey.' This partial compensation is insufficient for the low coverage, failing to fully clarify parameter meanings.
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 action ('Resolve') and resource ('a Fatebook question') with specific resolution options ('YES/NO/AMBIGUOUS'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'edit_question' or 'delete_question' that might also modify questions, which prevents a perfect score.
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 such as 'edit_question' for other modifications or 'get_question' for viewing. There's no mention of prerequisites, context, or exclusions, leaving the agent with minimal usage direction.
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. It mentions the return type ('Question objects') and the effect of 'detailed=True', but lacks critical behavioral details such as pagination behavior (implied by 'cursor' but not explained), authentication needs (implied by 'apiKey' but not stated), rate limits, or error handling.
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 the core purpose and structured efficiently in three sentences. Each sentence adds value: listing with filtering, return type, and detailed mode. No wasted words 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?
Given 7 parameters with 0% schema coverage and no annotations, but with an output schema (which handles return values), the description is moderately complete. It covers the basic operation and detailed mode, but misses key parameter semantics and behavioral context, making it adequate but with clear gaps.
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 explains 'detailed=True' adds fields, but doesn't clarify other parameters like 'resolved/unresolved' (mutually exclusive?), 'searchString' (what fields?), 'limit' (max?), or 'cursor' (pagination token). The description adds some value but leaves many parameters underspecified.
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 verb ('List') and resource ('Fatebook questions') with optional filtering, making the purpose specific. However, it doesn't explicitly differentiate from sibling tools like 'get_question' (singular) or 'count_forecasts', leaving room for minor ambiguity in sibling comparison.
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 description implies usage for listing questions with filtering, but doesn't provide explicit guidance on when to use this tool versus alternatives like 'get_question' for a single question or 'count_forecasts' for counts. No exclusions or prerequisites are mentioned.
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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