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tannerpace

Oracle Database MCP Server

by tannerpace

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: get_database_schema retrieves metadata about the database structure, while query_database executes SQL queries for data retrieval. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (get_database_schema, query_database) with clear, descriptive names that align with their functions. The naming is uniform and predictable.

    Tool Count2/5

    With only 2 tools, the server feels under-scoped for an Oracle Database MCP Server, as it lacks essential operations like data manipulation (INSERT, UPDATE, DELETE), transaction management, or administrative tasks, making it incomplete for typical database workflows.

    Completeness2/5

    The tool surface is severely incomplete for a database server, covering only schema inspection and read-only queries. Missing are critical operations such as data modification, stored procedure execution, user management, and other core database functionalities, leading to significant gaps.

  • Average 4/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • 1 of 1 community issues answered or closed in the last 6 months
    • 29 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
  • This repository is licensed under AGPL 3.0.

  • 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.

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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

  • Behavior3/5

    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 describes the tool's conditional behavior based on the tableName parameter, which is useful context. However, it doesn't disclose important behavioral traits like whether this requires specific permissions, what 'accessible tables' means in terms of access control, error handling for invalid table names, or response format details.

    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 perfectly concise with two sentences that efficiently convey all necessary information. The first sentence states the core purpose, and the second explains the conditional behavior. Every word earns its place with zero waste or redundancy.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a read-only schema inspection tool with no annotations and no output schema, the description provides adequate basic information about what the tool does and how parameters affect behavior. However, it lacks details about return format, error conditions, access restrictions, or what 'accessible tables' encompasses, which would be helpful given the absence 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?

    The input schema has 100% description coverage, with the tableName parameter clearly documented as optional. The description adds value by explaining the semantic impact of providing vs. not providing this parameter: it changes the return type from column details to a table list. However, it doesn't add syntax or format details 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.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the verb 'Get' and resource 'database schema information', with specific conditional behavior: returns column details for a specific table if tableName is provided, otherwise returns a list of all accessible tables. This distinguishes it from the sibling tool 'query_database', which presumably executes queries rather than retrieving schema metadata.

    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 on when to use the tool: use with tableName parameter to get column details for that table, or without parameter to get a list of all tables. However, it doesn't explicitly state when NOT to use it or mention alternatives like the sibling 'query_database' tool, which could be relevant for schema exploration vs. data querying.

    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 effectively states the tool is 'read-only', which implies safety from mutations, and mentions return types and execution metrics, adding useful context. However, it lacks details on permissions, rate limits, error handling, or database-specific constraints, which are important for a database query 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 front-loaded and concise, consisting of two sentences that efficiently convey the tool's purpose, constraints, and outputs without any wasted words. Every sentence earns its place by providing essential information, making it easy to understand at a glance.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's complexity (database querying with multiple parameters) and the absence of annotations and output schema, the description does a good job by specifying the query type, database, and return data. However, it could be more complete by including details on output format, error responses, or connection requirements, which would help an agent use it more 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 input schema has 100% description coverage, so the schema already documents all parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema, such as query syntax examples or default values for optional parameters. This meets the baseline score of 3, 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.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose with specific verbs ('Execute a read-only SQL SELECT query') and resources ('against the Oracle database'), and distinguishes it from potential siblings by specifying it's for SELECT queries only. It explicitly mentions what it returns ('rows, column names, and execution metrics'), making the purpose unambiguous and comprehensive.

    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 by specifying 'read-only SQL SELECT query' and 'SELECT statements only', which implicitly guides usage for data retrieval rather than modifications. However, it does not explicitly mention when not to use it or name alternatives like 'get_database_schema' for schema queries, leaving some room for improvement in sibling differentiation.

    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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