Oracle MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no ambiguity: describe_table focuses on column structure, execute_query handles SQL queries, get_table_constraints retrieves constraint details, get_table_indexes covers indexes, list_schemas enumerates schemas, and list_tables lists tables. There is no overlap in functionality that would cause misselection.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern using snake_case: describe_table, execute_query, get_table_constraints, get_table_indexes, list_schemas, and list_tables. The naming is predictable and readable throughout the set.
Tool Count5/5
With 6 tools, the server is well-scoped for database exploration and querying. Each tool earns its place by covering essential operations like schema listing, table metadata retrieval, and query execution, without being too sparse or bloated.
Completeness4/5
The tool set provides strong coverage for database inspection and querying, including CRUD-like operations via execute_query and metadata access. Minor gaps exist, such as no explicit tools for data manipulation (e.g., insert, update, delete) or transaction management, but agents can work around these using execute_query for most workflows.
Average 3/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 7 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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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. While 'Get' implies a read-only operation, the description doesn't specify whether this requires specific permissions, what happens if the table doesn't exist, whether it returns metadata in a structured format, or if there are any rate limits. For a 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 by specifying what is retrieved ('table structure') and what details are included ('columns, data types, and constraints'). There's no redundancy 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?
Given no annotations and no output schema, the description is incomplete for a tool that retrieves metadata. It doesn't describe the return format (e.g., structured JSON, plain text), what happens on errors, or whether the output includes additional details like column defaults or foreign keys. For a metadata retrieval tool with rich sibling tools, 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 the schema already documents both parameters ('table_name' and 'schema') with clear descriptions. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain format requirements for table names or default behavior for schema searches). 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 ('Get') and resource ('table structure') with specific details about what information is included ('columns, data types, and constraints'). It distinguishes from siblings like 'list_tables' (which would list names only) and 'get_table_constraints' (which would focus only on constraints). However, it doesn't explicitly mention how it differs from 'get_table_indexes' or 'execute_query' for structure queries.
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 this tool is preferred over 'get_table_constraints' (for just constraints) or 'execute_query' (for custom SQL queries about table structure). There's no context about prerequisites, error conditions, or performance considerations.
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 what the tool does but doesn't cover critical aspects like whether it supports read/write operations, transaction handling, error behavior, or performance implications (e.g., timeouts, resource limits). The mention of 'Oracle database' adds some context, but overall behavioral traits are largely 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?
The description is extremely concise—a single sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core action and resource, making it efficient for quick understanding. Every word earns its place, with no redundant or verbose phrasing.
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 database query tool with no annotations and no output schema, the description is incomplete. It doesn't explain return values (e.g., result sets, error formats), behavioral constraints, or usage prerequisites. For a tool that could involve data mutation or resource-intensive operations, more context is needed to ensure 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.
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 all three parameters (query, params, maxRows). The description adds no additional parameter semantics beyond what's in the schema, such as query syntax examples, parameter binding details, or maxRows implications. This meets the baseline of 3 since the schema adequately covers parameter meanings.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Execute') and resource ('SQL query on the Oracle database'), making the purpose immediately understandable. It distinguishes from siblings like 'describe_table' or 'list_tables' by focusing on query execution rather than metadata retrieval. However, it doesn't explicitly differentiate from potential non-sibling alternatives like 'execute_update' or 'execute_procedure' that might exist 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 whether this is for read-only queries, data modification, or both, nor does it specify prerequisites like database connections or permissions. Given siblings like 'describe_table' for metadata, there's no explicit differentiation in 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves constraints but doesn't describe the return format, pagination, error handling, or any limitations (e.g., access permissions, rate limits). For a read operation with no annotation coverage, this is a significant gap, as the agent lacks essential context for 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 a single, efficient sentence that directly states the tool's purpose without any fluff. It's appropriately sized and front-loaded, with every word contributing to clarity. This is an example of optimal conciseness for a simple tool.
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 read operation with 2 parameters) and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., format of constraints), potential errors, or how it relates to sibling tools. For a tool that retrieves database metadata, more context is needed to ensure the agent can use it correctly.
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%, meaning the input schema fully documents the parameters ('table_name' and 'schema'). The description doesn't add any semantic details beyond what the schema provides (e.g., it doesn't explain constraint types further or parameter interactions). With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get constraints (primary keys, foreign keys, unique, check) for a table'. It specifies the verb ('Get') and resource ('constraints for a table'), including the types of constraints. However, it doesn't explicitly differentiate from sibling tools like 'describe_table' or 'get_table_indexes', which might also provide constraint information, so it doesn't reach 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 'describe_table' (which might include constraints) or 'get_table_indexes', nor does it specify prerequisites or exclusions. This lack of context leaves the agent to infer usage, which is minimal guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Get indexes') but lacks details on permissions needed, rate limits, whether it's read-only or has side effects, or what the output format might be. This is a significant gap for a tool that interacts with database structures.
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 any wasted words. It's front-loaded and appropriately sized for a simple tool, 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., index names, types, columns), potential errors, or how it fits into broader database operations. For a tool with 2 parameters and no structured output, more context is needed to guide effective use.
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 input schema already documents both parameters ('table_name' and 'schema') clearly. The description adds no additional meaning beyond what's in the schema, such as examples or constraints, so it meets the baseline for adequate but not exceptional 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 tool's purpose with a specific verb ('Get') and resource ('indexes for a specific table'), making it easy to understand what it does. However, it doesn't differentiate from sibling tools like 'describe_table' or 'get_table_constraints', which might also provide table metadata, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. For example, it doesn't mention if this is for database optimization, debugging, or how it differs from 'describe_table' or 'get_table_constraints' in the sibling list. This leaves the agent without context 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 full burden but offers minimal behavioral context. It implies a read-only operation by using 'List', but doesn't disclose permissions needed, rate limits, pagination behavior, or what 'accessible schemas' means in practice. The description is too vague to fully inform the agent about behavioral traits.
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 listing tool and front-loads the core functionality.
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 simple read operation with 100% schema coverage but no annotations or output schema, the description is minimally adequate. It covers the basic purpose but lacks important context about behavioral aspects, usage guidelines, and what the output will contain. The absence of output schema means the description should ideally hint at return format.
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 input schema already documents both parameters thoroughly. The description adds marginal value by mentioning the schema parameter's optional nature and the concept of 'accessible schemas', but doesn't provide additional syntax, format details, or examples beyond what the schema provides.
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 ('tables'), specifying the scope ('from specified schema or all accessible schemas'). It distinguishes from siblings like 'list_schemas' by focusing on tables rather than schemas. However, it doesn't explicitly differentiate from other table-related tools like 'describe_table' or 'get_table_constraints'.
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_schemas' for schema listing or 'describe_table' for detailed table information. It mentions the optional schema parameter but doesn't explain usage scenarios or prerequisites.
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 lists schemas but doesn't describe what 'list' entails—whether it returns names only, full metadata, pagination behavior, permission requirements, or potential side effects. For a 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 zero-parameter tool and front-loads 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 tool with no output schema, the description is minimally adequate but lacks depth. It doesn't explain what 'list' returns (e.g., schema names, metadata, format) or how it differs from sibling tools. Given the simplicity of the tool, the description meets basic needs but could be more informative.
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 schema description coverage is 100% (since there are no parameters to document). The description appropriately doesn't discuss parameters, which aligns with the empty input schema. No additional parameter semantics are needed or provided.
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') and target resource ('all schemas in the database'), making the tool's purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_tables' or 'describe_table', which would require mentioning that this tool returns schema-level metadata rather than table-level 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_tables' or 'describe_table'. There's no mention of prerequisites, context for usage, or comparisons to sibling tools, leaving the agent to infer appropriate usage scenarios.
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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