MCP-MongoDB-MySQL-Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation3/5
The tools are clearly separated between MySQL and MongoDB operations, but within each database type there is some overlap. For example, 'execute' handles INSERT/UPDATE/DELETE for MySQL while 'query' handles SELECT, but these could potentially be confused with the more specific MongoDB equivalents like 'mongodb_insert' and 'mongodb_find'. The descriptions help clarify, but the boundaries aren't perfectly distinct.
Naming Consistency2/5
The naming is inconsistent across the set. MySQL tools use simple verb_noun patterns (e.g., 'add_column', 'create_table'), while MongoDB tools use a 'mongodb_' prefix followed by verb_noun (e.g., 'mongodb_create_collection', 'mongodb_find'). This mixing of conventions makes the set less predictable and harder to navigate at a glance.
Tool Count4/5
With 14 tools, the count is reasonable for a server covering two database systems (MySQL and MongoDB). It provides core operations for both, though it might be slightly heavy if considered as a single domain. Each tool appears to serve a distinct function, so the count feels appropriate for the scope.
Completeness4/5
For MySQL, the tools cover key operations like table management (create, describe, list), column addition, and query execution (SELECT, INSERT/UPDATE/DELETE). For MongoDB, they cover collection management (create, list) and CRUD operations (insert, find, update, delete). Minor gaps include missing operations like dropping tables/collections or more advanced queries, but core workflows are well-covered.
Average 2.9/5 across 14 of 14 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 status not available
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.
This repository includes a glama.json configuration file.
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.
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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. 'Get table structure' implies a read-only operation, but it doesn't specify what 'structure' includes (e.g., columns, data types, constraints), whether it requires specific permissions, or how errors are handled (e.g., if the table doesn't exist). 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 extremely concise at three words, with zero wasted language. It's front-loaded with the core action ('Get'), making it easy to scan and understand quickly. Every word earns its place by contributing to the tool's purpose.
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 database operations and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'structure' entails (e.g., schema details), potential outputs, or error conditions, which are critical for an agent to use this tool effectively in context with siblings like 'query' or 'create_table'.
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 the 'table' parameter clearly documented as 'Table name'. The description adds no additional meaning beyond this, as it doesn't elaborate on parameter syntax, format, or examples. With high schema coverage, the baseline score of 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.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get table structure' clearly indicates the tool retrieves metadata about a database table, which is a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'list_tables' (which likely lists table names) or 'query' (which might return data from tables), leaving room for ambiguity about its exact scope compared to 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. It doesn't mention prerequisites (e.g., needing a connected database), exclusions (e.g., not for querying data), or comparisons to siblings like 'list_tables' or 'query', leaving the agent to infer usage context 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?
No annotations are provided, so the description carries full burden for behavioral disclosure. 'Execute a SELECT query' implies a read-only operation, but it doesn't specify permissions needed, potential side effects (e.g., read locks), error handling, or return format. For a database query tool with zero annotation coverage, this is insufficient to inform safe and effective use.
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 three words, with zero wasted language. It's front-loaded with the core action and resource, making it easy to parse quickly. This efficiency is appropriate for a simple tool, though it may sacrifice completeness for brevity.
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 database queries, lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like safety, performance implications, or result formatting. While the schema handles parameters well, the overall context for reliable tool invocation is inadequate, especially compared to siblings that might overlap in functionality.
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 clear documentation for 'sql' (SQL SELECT query) and 'params' (query parameters). The description adds no additional parameter semantics beyond what the schema provides, such as SQL dialect constraints or parameter binding details. Given high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't detract either.
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 'Execute a SELECT query' clearly states the action (execute) and resource (SELECT query), making the purpose understandable. However, it doesn't distinguish this tool from sibling tools like 'execute' or 'mongodb_find' that might also perform query operations, leaving ambiguity about when to use this specific SQL query tool 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. With siblings like 'execute' (which might handle broader SQL operations) and 'mongodb_find' (for NoSQL queries), there's no indication of context, prerequisites, or exclusions. This lack of differentiation could lead to incorrect tool selection by an AI agent.
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. 'Add a new column' implies a write/mutation operation, but the description doesn't cover critical aspects: whether this requires specific permissions, if it's reversible, potential side effects (e.g., locking the table), or error conditions. For a schema-altering tool with zero annotation coverage, this is a significant gap in 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 a single, front-loaded sentence with zero wasted words—it directly states the tool's purpose. Every word earns its place, making it highly efficient and easy to parse. No extraneous details or redundancy are present, which is ideal for conciseness.
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 (schema-altering operation with nested parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral traits, parameter meanings, return values, or error handling. For a tool that modifies database schemas, this minimal description leaves too many gaps for safe and effective use by 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%, meaning parameters are undocumented in the schema. The description mentions 'table' and 'field' implicitly but adds no semantic details: it doesn't explain what 'table' refers to (e.g., table name), what 'field' encompasses (e.g., column definition with properties like type), or provide examples. With two required parameters and nested objects, the description fails to 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.
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') and resource ('new column to existing table'), making the purpose immediately understandable. It distinguishes from siblings like 'create_table' (creates new tables) and 'describe_table' (reads metadata). However, it doesn't specify the database system or context, which could help differentiate from similar tools in other 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 an existing table), exclusions (e.g., not for modifying existing columns), or sibling tools like 'execute' (which might handle SQL directly) or 'create_table' (for initial schema). Without this context, the agent 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 'Create a new table' which implies a write/mutation operation, but doesn't mention permissions required, whether the operation is idempotent, error handling, or what happens on success/failure. For a tool that modifies database structure, this lack of detail is a significant gap in 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 a single, clear sentence that gets straight to the point with zero wasted words. It's appropriately sized for a basic tool description and front-loads the essential information. Every word earns its place by communicating the core functionality 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 database mutation tool with 3 parameters, no annotations, no output schema, and only 33% schema description coverage, the description is incomplete. It doesn't address behavioral aspects like permissions or side effects, provides minimal parameter guidance, and offers no context about the database system or constraints. The agent would need to guess about many important usage aspects.
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 only 33% (only the 'table' parameter has a description), leaving most parameters undocumented. The description adds no parameter semantics beyond what's implied by the tool name - it doesn't explain what 'fields', 'indexes', or their sub-properties mean, nor provide examples or constraints. With 3 parameters and poor schema coverage, the description fails to compensate adequately.
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') and resource ('new table in the database'), making the purpose immediately understandable. It distinguishes this tool from siblings like 'add_column' or 'describe_table' by focusing on table creation rather than modification or inspection. However, it doesn't specify what kind of database or table characteristics, leaving some ambiguity.
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 established database connection via 'connect_db'), nor does it differentiate from similar tools like 'mongodb_create_collection' for MongoDB-specific operations. Without context, an agent might struggle to choose between this and 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 full burden but only states the basic action. It doesn't disclose whether this establishes a persistent connection, requires authentication, has timeout/rate limits, returns a connection object, or what happens on failure. For a connection tool with zero annotation coverage, this leaves critical behavioral aspects unspecified.
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?
Extremely concise single sentence with zero wasted words. Front-loaded with the core purpose. Every word earns its place in this minimal description.
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 connection tool with 6 parameters, 0% required parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what a successful connection yields, error conditions, or how parameters interact. 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?
Schema description coverage is low (33%), with only 'url' parameter documented. The description adds value by clarifying the two connection methods ('URL or config'), which helps interpret the parameter set, but doesn't explain the relationship between URL and individual config parameters (host, user, etc.) or which approach is preferred.
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 ('Connect to MySQL database') and resource ('MySQL database'), specifying the connection method ('using URL or config'). It distinguishes from sibling tools like 'connect_mongodb' by specifying MySQL, but doesn't explicitly contrast with other database tools beyond the name.
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 on when to use this tool versus alternatives like 'connect_mongodb' or other database operations. The description implies it's for establishing a connection, but doesn't specify prerequisites, timing, or when to choose URL vs config 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 full burden for behavioral disclosure. It states the tool establishes a connection but doesn't describe what that connection enables, whether it's persistent, what authentication is needed, error handling, or what happens if connection fails. For a connection tool with zero 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that states the core purpose without any unnecessary words. It's front-loaded with the main action and doesn't waste space on 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 connection tool with no annotations and no output schema, the description is inadequate. It doesn't explain what successful connection enables, what the tool returns, error conditions, or how this fits with sibling MongoDB tools. The agent would struggle to use this effectively without additional 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?
Schema description coverage is 100%, so all parameters are documented in the schema. The description mentions 'URL or config' which aligns with the 'url' parameter but doesn't explain the relationship between parameters or when to use which. It adds minimal value beyond what's already in the 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 clearly states the action ('Connect to MongoDB database') and the method ('using URL or config'), which is specific and unambiguous. However, it doesn't explicitly distinguish this from sibling tools like 'connect_db' or other MongoDB tools, leaving some ambiguity about when to choose this specific connection method.
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 'connect_db' or other MongoDB operations. There's no mention of prerequisites, when this connection is needed, or what happens after connection. This leaves the agent without 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 full burden for behavioral disclosure. It states the tool executes data manipulation queries (INSERT, UPDATE, DELETE), implying mutation operations, but doesn't cover critical aspects like permissions required, transaction handling, error behavior, or output format. For a mutation tool with zero annotation coverage, this is a significant 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 extremely concise—a single sentence with zero waste. It's front-loaded with the core purpose and uses clear, direct language. Every word earns its place, 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 tool's complexity (executing mutable SQL queries), lack of annotations, and no output schema, the description is incomplete. It doesn't address safety concerns, error handling, or what the tool returns (e.g., row counts, success/failure). For a mutation tool in a database context, this leaves critical gaps for an agent.
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 ('sql' and 'params') adequately. The description adds no additional parameter semantics beyond what's in the schema (e.g., no examples of SQL syntax or param usage). Baseline 3 is appropriate when 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's purpose: 'Execute an INSERT, UPDATE, or DELETE query.' It specifies the verb ('execute') and the resource/action type (SQL queries for data manipulation). However, it doesn't explicitly differentiate from sibling tools like 'query' (which likely handles SELECT queries) or 'add_column'/'create_table' (DDL operations), so it's not a perfect 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. It doesn't mention sibling tools like 'query' for SELECT operations or DDL tools for schema changes, nor does it specify prerequisites (e.g., database connection). This leaves the agent with minimal 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 full burden but offers minimal behavioral insight. It states it creates a collection but doesn't disclose permissions needed, whether it overwrites existing collections, error conditions, or typical response format. For a mutation 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 with zero waste. It's front-loaded with the core action and resource, making it easy 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?
For a mutation tool with no annotations, no output schema, and complex nested parameters (the 'options' object), the description is incomplete. It doesn't explain what 'options' might include, potential side effects, or what success/failure looks like, leaving significant gaps for an AI agent.
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 ('collection' and 'options'). The description adds no additional meaning beyond implying the tool creates something, which is obvious from the name. Baseline 3 is appropriate when 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 ('Create') and resource ('new collection in MongoDB'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'create_table' or 'mongodb_list_collections', which would require mentioning it's specifically for MongoDB collections rather than general database tables.
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 established MongoDB connection via 'connect_mongodb'), nor does it clarify when to choose this over similar tools like 'create_table' for relational databases.
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 the tool deletes documents, implying a destructive mutation, but doesn't cover critical aspects like permissions required, whether deletions are permanent or reversible, rate limits, or what happens on success/failure. This is inadequate 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 a single, efficient sentence with zero waste—it directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, 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 of a destructive database operation with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., safety, permissions), output format, or error handling, which are crucial for an agent to use this tool correctly 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?
Schema description coverage is 100%, with clear descriptions for all parameters (collection, filter, many). The description doesn't add any meaning beyond what the schema provides (e.g., it doesn't explain filter syntax or 'many' implications), so it meets the baseline of 3 where 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 ('Delete documents') and resource ('from a MongoDB collection'), providing a specific verb+resource combination. However, it doesn't distinguish this tool from sibling tools like 'mongodb_update' or 'execute' which might also modify data, leaving room for improvement in sibling 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 (e.g., needing a connection first), exclusions, or comparisons to siblings like 'mongodb_update' for modifications or 'execute' for general queries, leaving the agent with no usage context.
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. While 'find' implies a read operation, it doesn't specify whether this requires specific permissions, what happens with large result sets, whether it's paginated, or what the return format looks like. For a database query tool with zero annotation coverage, this leaves significant behavioral questions unanswered.
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 gets straight to the point with zero wasted words. It's appropriately sized for a basic find operation and front-loads the 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 this is a database query tool with no annotations, no output schema, and complex parameters (including nested objects for filter and sort), the description is insufficient. It doesn't explain what the tool returns, how errors are handled, or provide any context about MongoDB-specific behavior. The description should do more to compensate for the lack of structured metadata.
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 all parameters are documented in the schema. The description doesn't add any parameter semantics beyond what the schema already provides - it doesn't explain MongoDB query syntax, sort format, or provide examples. The baseline of 3 is appropriate when the schema does all the parameter documentation work.
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 'find' and resource 'documents in a MongoDB collection', making the purpose immediately understandable. It distinguishes from obvious siblings like mongodb_insert, mongodb_update, and mongodb_delete by specifying a read operation. However, it doesn't explicitly differentiate from query or execute tools that might also retrieve data.
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 when to choose mongodb_find over query, execute, or other data retrieval methods. It also doesn't indicate prerequisites like needing an established connection 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 full burden for behavioral disclosure. It states the tool inserts documents, implying a write operation, but lacks details on permissions required, whether it's idempotent, error handling (e.g., duplicate keys), or response format. 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, direct sentence with zero wasted words, making it highly efficient and front-loaded. It immediately conveys the core function without unnecessary elaboration, earning full marks for conciseness.
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 write operation tool with no annotations and no output schema, the description is insufficient. It lacks critical details like expected return values, error conditions, or behavioral traits (e.g., atomicity, performance implications), leaving the agent with incomplete operational 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?
Schema description coverage is 100%, with clear descriptions for both parameters ('collection' and 'documents'). The description adds no additional semantic context beyond what the schema provides, such as format examples or constraints, so it meets the baseline for adequate but unenhanced 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 ('Insert') and target resource ('documents into a MongoDB collection'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'mongodb_create_collection' or 'mongodb_update', 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 like 'mongodb_update' for modifying existing documents or 'mongodb_create_collection' for creating collections. There's no mention of prerequisites, such as needing an existing collection, or contextual advice for 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 updates documents but fails to mention critical details like required permissions, whether updates are atomic or reversible, potential side effects on data integrity, or error handling. 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 front-loaded with the core purpose and appropriately sized for the tool's complexity, making it easy 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 tool's complexity (a mutation operation with nested objects and no output schema) and lack of annotations, the description is incomplete. It doesn't cover behavioral aspects like safety, permissions, or return values, which are crucial for an update tool in a database 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%, so the schema already documents all parameters ('collection', 'filter', 'update', 'many') adequately. The description adds no additional meaning beyond what the schema provides, such as examples or usage context, which aligns with the baseline score 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 ('Update') and resource ('documents in a MongoDB collection'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'mongodb_delete' or 'mongodb_insert' beyond the verb, 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. It doesn't mention sibling tools like 'mongodb_insert' for creating documents or 'mongodb_delete' for removing them, nor does it specify prerequisites such as needing a connection 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'List all tables' implies a read-only operation, it doesn't specify whether this requires database permissions, how results are formatted, if there are pagination limits, or what happens with empty databases. For a tool with zero annotation coverage, this leaves significant 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 function with zero wasted words. It's appropriately front-loaded and earns its place by clearly conveying the core 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 the lack of annotations and output schema, the description is insufficient for a database tool. It doesn't explain what the output looks like (e.g., table names, metadata), whether it works across different database types, or any error conditions. For a tool that likely returns structured data, this leaves too much undefined.
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 with 100% schema description coverage, so the schema already fully documents the lack of inputs. The description doesn't need to add parameter information, and it correctly implies no filtering or options are available ('all tables'). This meets the baseline expectation for parameterless tools.
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 ('all tables in the database'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'describe_table' or 'query', but the action is specific enough to avoid confusion with most siblings.
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 siblings like 'describe_table' (for table details) and 'query' (for executing SQL), there's no indication of when listing tables is appropriate versus other database 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 but offers minimal information. It states it's a list operation (implying read-only), but doesn't cover important aspects like whether it requires authentication, returns paginated results, includes system collections, or what format the output takes. This leaves significant gaps for an agent to understand 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 communicates the core purpose without any wasted words. It's appropriately sized for a simple listing operation and is front-loaded with the essential information.
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 zero-parameter listing tool with no output schema, the description is minimally complete - it tells the agent what the tool does. However, without annotations and with sibling tools that perform similar functions in different contexts, more guidance would be helpful. The description doesn't address how this differs from 'list_tables' or when to use MongoDB-specific versus generic database tools.
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 with 100% schema description coverage, so the schema already fully documents the parameter situation. The description doesn't need to add parameter information, and it appropriately doesn't mention any parameters. This meets the baseline expectation for a zero-parameter tool.
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 ('List all collections') and target resource ('in the MongoDB database'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'list_tables' or 'describe_table', which reduces its score from a perfect 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. There's no mention of prerequisites (e.g., needing a connection first), comparison to similar tools like 'list_tables' for SQL databases, or context about when listing collections is appropriate versus other MongoDB operations.
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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