Convolut MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Every tool has a clearly distinct purpose with no ambiguity. For example, create_context, get_context, update_context, and delete_context form distinct CRUD operations, while consolidate_contexts, plan_from_contexts, and search_contexts each serve unique AI-driven functions. The descriptions clearly differentiate overlapping tools like list_contexts (filtering) and search_contexts (semantic search).
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case, such as create_context, delete_context, and get_context_stats. There are no deviations in naming conventions, making the set predictable and readable. This uniformity aids in agent selection and understanding of tool purposes.
Tool Count5/5
With 11 tools, the count is well-scoped for a context management server, covering core operations without bloat. Each tool earns its place by addressing specific needs like CRUD, search, export, and AI analysis. This aligns with typical server scopes of 3-15 tools, ensuring comprehensive coverage without overwhelming complexity.
Completeness5/5
The tool surface provides complete CRUD/lifecycle coverage for context management, including create, get, update, delete, list, and search. It extends beyond basics with AI features (consolidate, plan), utilities (export, raw URL), and analytics (stats), leaving no obvious gaps. Agents can perform end-to-end workflows without dead ends.
Average 3/5 across 11 of 11 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions AI usage, which hints at potential non-deterministic or resource-intensive behavior, but doesn't specify details like rate limits, authentication needs, output format, or whether it's a read-only or mutative operation. The description is too brief to adequately cover behavioral traits beyond the basic purpose.
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 a single, efficient sentence that directly states the tool's purpose. It's appropriately sized and front-loaded with the core action, though it could be slightly more informative without sacrificing brevity. There's no wasted text, making it concise and well-structured.
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 AI-based planning with contexts, the description is incomplete. No annotations exist to clarify behavior, and there's no output schema to describe return values. The description doesn't explain what 'actionable plans' entail, how results are formatted, or any dependencies on other tools. This leaves significant gaps for an agent to understand the tool's full context and usage.
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 100%, so the schema already documents both parameters ('context_ids' and 'planning_prompt') with descriptions and constraints. The tool description adds no additional meaning about parameters beyond what the schema provides, such as explaining what 'contexts' are or how the prompt influences planning. This meets the baseline for high schema coverage.
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 'Analyze contexts and generate actionable plans using AI' states a clear purpose with a verb ('analyze' and 'generate') and resource ('contexts'), but it's somewhat vague about what 'contexts' are and doesn't distinguish this tool from siblings like 'consolidate_contexts' or 'search_contexts' that might also involve context analysis. It specifies AI involvement, which adds some differentiation.
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, such as needing existing contexts from tools like 'create_context' or 'list_contexts', or when to prefer this over other planning-related tools (if any exist). Usage is implied only by the tool name and description, with no explicit context 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions AI involvement and the outcome ('consolidated summary or composition'), but lacks critical details: it doesn't specify if this is a read-only or destructive operation, what permissions are required, how long processing takes, or what the output format looks like. For a tool that merges data with AI, this gap is significant and limits predictability.
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 action ('Merge multiple contexts using AI') and outcome ('create a consolidated summary or composition'). There is no wasted verbiage or redundancy, making it easy to parse quickly while conveying essential 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?
Given the complexity of merging contexts with AI, lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like safety, performance, or output format, which are crucial for an AI-driven tool. While the schema covers parameters well, the overall context for reliable tool invocation is insufficient, especially compared to siblings that may have clearer purposes.
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 100%, providing detailed parameter documentation (e.g., 'context_ids' as UUIDs with min/max items, 'consolidation_type' with enum values). The description adds minimal value beyond the schema, only implying that 'consolidation_type' affects the output ('summary or composition'). It doesn't explain the difference between 'summarize' and 'compose' or how 'custom_prompt' influences the process, so it meets the baseline but doesn't enhance understanding.
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: 'Merge multiple contexts using AI to create a consolidated summary or composition.' It specifies the verb ('merge'), resource ('contexts'), and outcome ('consolidated summary or composition'), distinguishing it from siblings like 'list_contexts' or 'search_contexts' that don't involve merging. However, it doesn't explicitly differentiate from 'plan_from_contexts', which might have overlapping functionality, preventing 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. It doesn't mention when to choose 'summarize' vs. 'compose', or how this differs from siblings like 'plan_from_contexts' or 'export_contexts'. Without such context, users must infer usage from the tool name and parameters alone, which is insufficient for effective 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. While 'Create' implies a write operation, the description doesn't mention permission requirements, whether the operation is idempotent, what happens on duplicate titles, or what the response looks like. For a creation tool with zero annotation coverage, this 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.
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 action and key parameters. There's zero wasted language or redundancy, making it maximally concise while still conveying essential 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 creation tool with 6 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't address behavioral aspects like permissions or idempotency, doesn't explain the relationship to sibling tools, and doesn't provide usage context. The high parameter count and mutation nature demand more comprehensive guidance.
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 100%, so the schema already documents all 6 parameters thoroughly. The description mentions 'title, content, tags, and metadata' which maps to some parameters but omits 'category', 'is_favorite', and 'files'. Since the schema does the heavy lifting, the baseline score of 3 is appropriate despite the incomplete parameter listing in the description.
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 ('Create a new context') and specifies the key components (title, content, tags, metadata), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'update_context' or explain what distinguishes 'context' from other entities in the system.
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 'update_context' or 'consolidate_contexts'. There's no mention of prerequisites, constraints, or typical scenarios where this creation operation is appropriate versus other operations on contexts.
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. It states 'permanently', which implies irreversibility, but doesn't cover critical aspects like required permissions, side effects (e.g., if deletion affects related data), error conditions, or confirmation steps, making it insufficient 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, direct sentence with zero waste—'Delete a context permanently by its ID'—front-loading the key action and resource efficiently. It's appropriately sized for the tool's simplicity.
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 tool's destructive nature, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., permissions, consequences), usage context, and return values, which are essential for safe and effective invocation in a system with multiple context-related tools.
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 100%, with the parameter 'context_id' fully documented in the schema as a UUID. The description adds no additional meaning beyond implying deletion targets a specific context by ID, so it meets the baseline for high schema coverage without compensating further.
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 ('Delete') and resource ('a context permanently by its ID'), making the purpose unambiguous. However, it doesn't differentiate this tool from other deletion-related operations that might exist among siblings, such as 'consolidate_contexts' which could involve removal, so it lacks explicit 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. With sibling tools like 'consolidate_contexts', 'update_context', and 'list_contexts', there's no indication of prerequisites, exclusions, or scenarios favoring deletion over other operations, leaving usage unclear.
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 full burden. It mentions export for integration but lacks critical behavioral details: whether this is a read-only operation, if it requires specific permissions, what the output looks like (file, data structure), or any rate limits. The description is too vague for a mutation-like 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 waste. It front-loads the core purpose and includes key details (formats, integration use) 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?
For a tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like safety, output format, or integration specifics, leaving significant gaps for an agent to understand how to use it effectively.
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 100%, so the schema fully documents all parameters. The description adds no parameter-specific information beyond mentioning formats (already in schema) and doesn't explain context IDs or metadata further. Baseline 3 is appropriate as the schema does the heavy lifting.
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 exports contexts in various formats for integration purposes, specifying the verb (export) and resource (contexts). It distinguishes from siblings like list_contexts or get_context by focusing on export functionality, though it doesn't explicitly contrast with them.
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. With siblings like get_context (single context retrieval) and list_contexts (listing without export), the description doesn't help an agent choose between them or mention prerequisites like needing context IDs first.
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. While it mentions 'advanced options including keywords, tags, categories, and date ranges', it doesn't address important behavioral aspects like whether this is a read-only operation, what permissions are required, how results are ordered, or what happens when no filters are applied. For a tool with 8 parameters and no annotation coverage, this is insufficient.
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 a single, efficient sentence that front-loads the core purpose. It wastes no words and directly communicates the tool's function, though it could be slightly more structured by separating the core action from the feature list.
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 tool with 8 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what 'contexts' are in this system, what format results return, whether this is paginated (though offset/limit parameters suggest it is), or how filtering combinations work. The agent lacks sufficient context to use this tool effectively.
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 description mentions the filtering capabilities (keywords, tags, categories, date ranges) which aligns with some parameters, but with 100% schema description coverage, the schema already fully documents all 8 parameters. The description adds minimal value beyond what's in the schema, meeting the baseline expectation when schema coverage is complete.
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 as 'Search and filter contexts with advanced options', which specifies both the verb (search/filter) and resource (contexts). It distinguishes itself from basic list operations by mentioning 'advanced options', but doesn't explicitly differentiate from the sibling 'search_contexts' tool, which appears to have overlapping functionality.
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 'search_contexts' or 'get_context'. It mentions 'advanced options' but doesn't clarify what makes this tool different from other context-related tools in the server, leaving the agent without clear 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 provided, the description carries full burden but offers minimal behavioral insight. It mentions 'semantic search' but doesn't explain how this differs from keyword search, what algorithm is used, or performance characteristics. It lacks details on permissions, rate limits, error handling, or what 'relevant information' means in the return. For a search tool with zero annotation coverage, this is inadequate.
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. Every word earns its place with no redundancy or fluff. It's appropriately sized for a search tool, 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (semantic search across contexts with 4 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'contexts' are, how results are ranked, the format of returned information, or error cases. For a tool with moderate complexity and no structured behavioral hints, more context is needed.
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 100%, so parameters are well-documented in the schema itself. The description adds no additional parameter semantics beyond implying the search is 'semantic' (which relates to the 'query' parameter). It doesn't clarify interactions between parameters (e.g., how 'category' and 'tags' combine) or provide examples. Baseline 3 is appropriate since the schema does the heavy lifting.
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 ('perform semantic search') and resource ('across contexts'), making the purpose immediately understandable. It distinguishes this from siblings like 'list_contexts' (which likely returns all contexts without search) and 'get_context' (which retrieves a specific context). However, it doesn't explicitly mention what 'contexts' are in this system, which slightly reduces specificity.
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 semantic search is preferable to filtering with 'list_contexts' or using other siblings like 'plan_from_contexts'. There are no prerequisites, exclusions, or comparisons to help the agent choose appropriately among the 10 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is an update operation but doesn't mention permission requirements, whether updates are partial or complete, if changes are reversible, error handling, or rate limits. For a mutation tool with zero annotation coverage, this 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.
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. Every word earns its place with no redundant information or 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?
For a mutation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what happens on success/failure, return values, or important behavioral aspects like whether all fields must be provided or if updates are partial. Given the complexity of updating a context with multiple fields, more contextual information is needed.
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 100%, so the schema already documents all 6 parameters thoroughly. The description mentions 'title, content, tags, or metadata' which maps to some parameters but omits 'category' and 'is_favorite' from its list. It adds minimal value beyond what the schema provides, meeting the baseline for high schema coverage.
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 ('Update') and resource ('an existing context'), and specifies what can be updated ('new title, content, tags, or metadata'). It distinguishes from 'create_context' by focusing on existing contexts, but doesn't explicitly differentiate from other siblings like 'consolidate_contexts' or 'plan_from_contexts'.
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 a valid context_id), when not to use it, or how it differs from other update-related operations that might exist among siblings.
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 states the tool retrieves content and metadata, implying a read-only operation, but doesn't disclose behavioral traits like error handling (e.g., what happens if the ID is invalid), performance (e.g., response time), or side effects (e.g., caching). This leaves significant gaps for a tool with no 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 a single, efficient sentence that front-loads the core purpose ('Retrieve a specific context by its ID') and adds necessary detail ('including full content and metadata'). There is no wasted wording, and it is appropriately sized for a simple retrieval tool.
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 (single parameter, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and what is returned, but lacks usage guidelines, behavioral details, and output information. This makes it minimally viable but with clear gaps in context.
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 description coverage is 100%, with the single parameter 'context_id' documented as a UUID for identifying the context. The description adds minimal value beyond the schema by implying the ID is used to retrieve the context, but doesn't provide additional semantics like format examples or constraints. This meets the baseline of 3 for high schema coverage.
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 ('Retrieve') and target ('a specific context by its ID'), and specifies what is included ('full content and metadata'). It distinguishes from siblings like list_contexts (which lists multiple) or get_context_stats (which provides statistics). However, it doesn't explicitly differentiate from get_raw_url or other retrieval siblings, keeping it at 4 rather than 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?
The description provides no guidance on when to use this tool versus alternatives like list_contexts (for browsing), search_contexts (for filtering), or get_raw_url (for raw data). It mentions retrieving by ID but doesn't specify prerequisites (e.g., needing a valid context_id) or exclusions (e.g., not for bulk operations).
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 retrieves statistical information but doesn't describe what format the statistics come in, whether there are rate limits, authentication requirements, or how the data is aggregated. For a statistical tool with zero annotation coverage, this 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized for a simple retrieval tool and front-loads the essential information about what statistical data is available.
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?
For a statistical retrieval tool with 2 well-documented parameters but no annotations and no output schema, the description provides basic purpose but lacks important context about return format, data aggregation methods, and behavioral characteristics. It's minimally adequate but has clear gaps in 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 100% description coverage with clear enum values and defaults for both parameters. The description doesn't add any parameter-specific information beyond what's already documented in the schema. According to scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description.
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 as retrieving statistical information about contexts, specifying the types of data included (counts, categories, usage metrics). It uses a specific verb ('Get') and identifies the resource ('contexts'), but doesn't explicitly differentiate from sibling tools like 'list_contexts' or 'search_contexts' that might also provide contextual information.
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 'list_contexts' or 'search_contexts' that might also retrieve context information. It doesn't mention prerequisites, exclusions, or specific scenarios where statistical data is preferred over raw listings or searches.
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 burden of behavioral disclosure. It does well by specifying the temporary nature (10-minute expiration) and authentication bypass capability, but doesn't mention rate limits, error conditions, or what happens after expiration. For a tool that generates access URLs with security implications, more behavioral context would be helpful.
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 communicates the core functionality, key constraints (temporary, no authentication), and expiration timeframe. Every word earns its place with zero wasted text, making it easy to parse and understand quickly.
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?
For a single-parameter tool with no annotations and no output schema, the description provides adequate but minimal coverage. It explains what the tool does and key behavioral aspects, but doesn't describe the return format (what the raw URL looks like) or potential error conditions. The description is complete enough for basic understanding but leaves operational details unspecified.
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 100%, so the schema already fully documents the single 'context_id' parameter. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 for good schema 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 the specific action ('Generate a temporary raw URL') and resource ('for a context'), distinguishing it from sibling tools like get_context or list_contexts. It precisely defines the tool's unique function of creating an unauthenticated access URL rather than retrieving context data directly.
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 context for when to use this tool ('accessed without authentication', 'expires in 10 minutes'), but doesn't explicitly state when NOT to use it or name specific alternatives. It implies this is for temporary sharing scenarios rather than persistent access, which helps differentiate from other context tools.
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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