DevVault MCP

by cookieark7

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes: querying, writing, seeding, and introspection. However, run_query and run_write are clear opposites, and count_rows overlaps slightly with db_info/list_tables in terms of database introspection, though they serve different specific needs.
Naming Consistency5/5
All tool names use a consistent verb_noun pattern: run_query, run_write, seed_dummy, db_info, list_tables, describe_table, count_rows. Each name clearly indicates the action and the target, making the set highly predictable.
Tool Count4/5
With 7 tools, the count is well within the ideal range for a database management server. Each tool covers a core operation (query, write, seed, introspect), and none feel redundant. A couple more tools for advanced operations (e.g., transactions, backups) could be added, but the current count is appropriate.
Completeness4/5
The server covers essential database operations: querying, writing, seeding dummy data, and introspection (list tables, describe schema, row counts). It lacks tools for schema modification or data deletion scoped by ID, but the primary workflows for development (read, write, seed, introspect) are well covered.
Average 4.2/5 across 7 of 7 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It states the tool returns exact counts but reveals no behavioral details such as whether it is read-only, requires permissions, or has performance implications. It does not mention any side effects or limitations.
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 is front-loaded and concise. Every word serves a purpose, with no fluff or repetition.
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 is simple (no parameters, no output schema), the description covers the core purpose. However, it lacks additional context such as the return format or specific tables included, which could be helpful. It is adequate but not exhaustive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema coverage is 100% (none). The description does not need to explain parameters, as there are none. The description adds context about what is counted (main tables), which is clear.
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 provides 'exact row counts for the main DevVault tables,' which identifies the specific verb ('count') and resource ('main DevVault tables'). It is not a tautology and differentiates from siblings like run_query or db_info, though it does not explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for getting table sizes, but does not specify when to use this over run_query or db_info. There is no explicit guidance on when not to use it or mention of alternatives, making it less explicit than ideal.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of conveying safety. The word 'Show' implies a read-only operation, which is useful, but it does not disclose error behavior (e.g., what happens if the table doesn't exist) or whether any side effects occur. The description adds minimal behavioral context beyond the obvious read-only intent.
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 that states the action and the output attributes without any wasted words. It is appropriately concise for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one parameter, no output schema, no annotations), and the description covers the essential aspects: what it does and what fields are returned. It lacks details like error handling or prerequisites, but given the low complexity, it is nearly complete. The mention of returned columns compensates for the absence of an output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides 100% coverage for the single 'table' parameter with a clear description and example ('Table name, e.g. "snippets"'). The tool description does not add additional parameter semantics beyond what the schema already includes, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Show' with the resource 'columns for a table', listing the exact attributes returned (name, type, nullable, default). This clearly distinguishes it from sibling tools like list_tables (which lists tables) and count_rows (which counts rows).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The purpose is implicitly clear from the description: use this tool to inspect a table's schema. However, there is no explicit guidance on when to choose this over alternatives like run_query or list_tables, nor any mention of prerequisites (e.g., table existence or permission requirements).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses scope ('public tables') and the behavioral nuance of 'live row estimates,' but it does not mention output shape, ordering, or potential cost. This is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence that communicates the tool's purpose, scope, and a key behavioral detail without any wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple and has no parameters, and the description covers what the tool returns at a high level ('public tables with live row estimates'). It is complete enough for typical selection, though output details are left unspecified.
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?
There are zero parameters, so the baseline is 4. The description adds nothing about parameters because there is nothing to add; schema coverage is trivially complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') and resource ('public tables') and adds the scope of 'live row estimates,' making the tool's purpose distinct from siblings like describe_table and count_rows.
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 context is clear: use this to enumerate public tables and see approximate row counts. It does not explicitly state when to prefer alternatives, but the scope and phrasing make the intended use obvious for a zero-parameter listing tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that it inserts rows, uses valid cuid ids and timestamps, attaches to existing users, and returns the number of rows inserted. This gives the agent a solid understanding of side effects, though it could have mentioned edge cases like what happens if no users exist.
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 only two sentences long, gets straight to the point, and uses no fluffy language. Every word adds value, making it easy for the agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a seeding tool with no output schema and no annotations, the description covers the essential aspects: purpose, attachment behavior, and return value. It doesn't address idempotency or validation, but those may not be critical for a dummy data seeder in this 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 coverage is low (17%, only userId has a description). The description partially compensates by listing the target tables ('snippets/bookmarks/notes/commands/prompts') that match the parameter names, implying each parameter is a count. However, it doesn't explicitly state that the integer values represent row counts per table, leaving some ambiguity for the agent.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Insert' and enumerates the exact resources ('snippets/bookmarks/notes/commands/prompts'), clearly defining what the tool does. It differentiates itself from siblings like run_query and db_info by focusing on populating tables with dummy data. The title and description align perfectly.
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 phrase 'Great for filling the empty prompts/commands tables' provides a concrete scenario for when to use the tool. It doesn't explicitly mention alternatives, but the sibling tools are distinct enough that the intended usage is clear without further explanation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing side effects. It clearly indicates this performs writes surfaces safety guards, and explains how to obtain returned rows, covering the key runtime behaviors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, front-loaded with the core purpose and immediately followed by critical constraints. Zero filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a write tool with no output schema, the description covers the essential behavior (what it executes), safety guards, and return-value control. Minor gap: no statement about what happens without RETURNING (e.g., whether affected row count is returned), but this is acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% coverage, but the description adds valuable context: allowDangerous overrides guards and RETURNING usage. This goes beyond the schema's simple field descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool runs a single INSERT/UPDATE/DELETE statement, with a specific verb and resource. It distinguishes from siblings like run_query (reads) and other DB introspection tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit safety constraints (refuses DDL/unscoped writes unless allowDangerous=true) and practical guidance (add RETURNING to see rows). While it doesn't explicitly name alternatives like run_query, the scope is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden. It discloses the informational/read-only nature of the operation and explicitly notes that credentials are not exposed, which is a valuable safety detail for an unannotated 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 one clear, front-loaded sentence. It communicates purpose and scope without filler or redundancy, and every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a low-complexity, zero-parameter informational tool. The description is sufficient context: it states what is shown and explicitly excludes credentials; no deeper output contract is necessary for such a simple operation.
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, so the baseline of 4 applies. The description correctly adds no misleading parameter details because there are no parameters to explain.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Show') and clearly identifies the resource ('which database this server is connected to') with explicit scope ('host/db only'). This clearly separates it from query/run/write/listing sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies a clear use case: when you need to know what database the server is connected to. It also explicitly notes that credentials are excluded, giving a boundary, though it does not explicitly name alternative tools for other database operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, but the description explicitly states it executes within a read-only transaction, clarifying side-effects and parameter usage, providing transparency about 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 concise, consisting of three sentences conveying the essential information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the schema covers parameters, the description covers purpose, behavior, and parameter usage, making it complete for the tool's scope.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds context by explaining that $1, $2 placeholders are used with the params parameter, and reiterates the read-only nature, complementing the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it runs a read-only SQL statement, specifying allowed statement types (SELECT/WITH/EXPLAIN/SHOW), distinguishing it from write operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It specifies the tool is for read-only queries, implying use for data retrieval, and explains parameter placeholder usage, but does not explicitly mention when not to use or alternatives, though the sibling 'run_write' is evident.
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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