OceanBase Oracle MCP Server

Overview Schema Related Servers Score Discussions

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a distinct purpose: query_db for read queries, list_tables for listing tables, get_table_schema for column details, search_tables for keyword search, get_table_indexes for indexes, export_source for PL/SQL, explain_plan for execution plans, get_table_stats for statistics, and preview_write/confirm_write for safe write operations. No overlapping functionality.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (e.g., query_db, list_tables, get_table_schema, search_tables, get_table_indexes, export_source, explain_plan, get_table_stats, preview_write, confirm_write). No mixing of conventions.
Tool Count5/5
10 tools is well-scoped for a database server covering querying, schema inspection, indexing, statistics, execution plans, PL/SQL export, and write operations with a two-phase commit safety mechanism. Each tool earns its place.
Completeness5/5
The tool set covers all major operations for an Oracle OceanBase database: read queries, schema browsing (tables, columns, indexes), statistics, execution plans, PL/SQL export, and both preview and execution of write operations. No obvious gaps in the domain.
Average 3.8/5 across 10 of 10 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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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 provided, so description carries full burden. It mentions EXPLAIN but does not disclose that it is read-only, has no side effects, or requires any permissions. For a diagnostic tool, behavioral traits like safety are missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence clearly conveys purpose with no filler. Efficient and front-loaded, though could be slightly expanded without losing conciseness.
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 simple one-parameter tool, the description covers the essential purpose but lacks usage guidelines and output information. With no output schema, a brief note on return format would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a parameter description that adequately explains the input. The tool description adds little extra meaning beyond the schema, meeting the baseline.
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 explains SQL execution plans for performance analysis. It uses specific verbs (查看/explain) and resource (SQL execution plan), distinguishing it from siblings like query_db that execute 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?
No guidance on when to use this tool versus alternatives like query_db or when not to use it. Prerequisites (e.g., having a SQL query) are implied but not stated.
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 must fully disclose behavioral traits. It only lists output fields and does not mention read-only nature, required permissions, potential staleness of statistics, or any side effects. This is insufficient for safe agent invocation.
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, focused sentence that immediately conveys the tool's function and lists the key statistics. Every word earns its place with no redundancy, making it highly concise and efficiently front-loaded.
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 no output schema, the description adequately specifies the return fields (six statistics), which is moderately complete for an agent to understand what information is retrieved. However, it omits behavioral context like execution cost, access requirements, and return format, which are needed for complete understanding.
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% for the single parameter (table_name), so the schema already provides its meaning. The description adds no additional semantics beyond what the schema states, keeping the score at baseline.
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: getting table statistics. It explicitly lists the specific statistics returned (real row count, stats row count, avg row length, blocks, columns, last analysis time), matching the tool name and distinguishing it from siblings like get_table_schema.
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 get_table_schema or query_db. It lacks context such as prerequisites, performance implications, or scenarios where this tool is preferred, leaving the agent to infer usage from the 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. It only states the action (get indexes) without disclosing read-only nature, performance, prerequisites, or side effects. Minimal behavioral context.
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 sentence that directly states the purpose and key output fields. No unnecessary words, front-loaded and efficient.
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 no output schema, the description lists the output fields (index name, type, uniqueness, included columns), which is helpful. Could be more explicit about output format, but covers essential information.
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% (table_name with description). The description does not add additional parameter meaning beyond what is in the schema, meeting the baseline for adequate 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 it retrieves index information (name, type, uniqueness, included columns) for a specified table, matching the tool name and distinguishing it from siblings like get_table_schema.
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 but does not explicitly state when to use this tool over alternatives (e.g., get_table_schema). It lacks context about when not to use it.
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 must fully disclose behavior. It mentions saving to file and listing when no name, but lacks details on side effects such as file overwriting, permissions required, or whether the operation is destructive. The export action suggests mutation, but the description does not clarify if it modifies the database.
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 with two short sentences in Chinese, front-loading the purpose and key behavior. Every word adds value with no 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?
Given four parameters and no output schema or annotations, the description covers the main functionality (list vs export) and parameter defaults. However, it lacks details on file format, overwrite behavior, and error conditions, which are important for a file-writing tool.
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 coverage is 100%, but the description adds significant meaning: it explains that omitting name lists all objects, and for output_dir it specifies the default path from environment variable or hardcoded fallback. This goes beyond the schema's basic 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 exports PL/SQL source code (stored procedures, functions, packages) and saves to file. It also mentions that omitting the name parameter lists all source objects, distinguishing it from siblings like query_db and list_tables.
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 exporting source code but does not explicitly state when to use versus alternatives like query_db or get_table_schema. No exclusions or alternative tools are mentioned, providing only basic 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 provided, so the description bears the full burden. It discloses that table_name must be uppercase and that output includes column attributes, but omits read-only nature, error behavior for missing tables, performance implications, or any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, no redundant information. Efficiently conveys purpose and key constraint. However, could be slightly more structured with a separate sentence for usage conditions.
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 no output schema and only one parameter, the description adequately explains what the tool returns (column details) and input requirement (uppercase table name). It lacks mention of ordering or filtering, which is acceptable for a simple schema retrieval tool.
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 coverage is 100%, but the description adds value beyond the schema by specifying that table_name must be uppercase and for Oracle, and that the output includes specific column attributes. This clarifies input semantics and expected result.
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 'get' and resource 'table schema', listing the exact output columns (column name, data type, length, nullable). This clearly distinguishes it from sibling tools like list_tables (which lists table names) or get_table_indexes.
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 when you need column details for a known table, but does not explicitly state when to use it over alternatives like query_db or search_tables. It mentions table_name must be uppercase, but no exclusion criteria or prerequisites.
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?
No annotations provided, so the description carries full burden. It states the tool searches and returns results, implying a read operation, but does not explicitly disclose behavioral traits such as case-insensitivity (only in schema), pagination, or safety guarantees.
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 covers the tool's functionality without any redundant information. Every word is meaningful.
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 simple search tool with one parameter and no output schema, the description adequately states it returns a list of matching results. However, it does not specify what fields the results contain (e.g., just names or full details), which would be helpful given the lack of 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?
Schema description coverage is 100% for the single parameter. The description does not add any additional meaning beyond what the schema already provides (e.g., case-insensitivity mentioned in schema but not in description). 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 clearly specifies a verb 'search' and resource 'table and view names', distinguishing it from siblings like query_db (executes SQL) and list_tables (lists all tables without keyword).
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?
No explicit guidance on when to use this tool versus alternatives (e.g., query_db or list_tables). The description only states what it does, leaving the agent to infer usage context from the tool name and siblings.
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?
Discloses caching behavior (5-minute cache) and refresh mechanism. Without annotations, the description provides useful behavioral context beyond the schema.
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 sentences deliver purpose and caching details efficiently with no 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?
Covers main behavior and caching, but lacks description of the output format or return value, which would be helpful given no 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?
Schema description coverage is 100%, and the description reiterates the same information about the refresh parameter without adding new semantics.
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?
Description clearly states the tool lists tables and views accessible by the current user, distinguishing it from siblings like search_tables or get_table_schema.
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?
Provides context on caching and refresh but no explicit guidance on when to use this tool versus alternatives or when not to use it.
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 clearly discloses the read-only nature, automatic row limit (1000), and configurable max_rows (up to 10000). It also specifies the database backend (OceanBase Oracle mode). This is sufficient behavioral transparency for a simple read-only 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is concise (three sentences), front-loaded with the core purpose, and contains no extraneous information. It could be slightly more structured by separating usage guidelines from behavior, but overall it is efficient.
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 no output schema, the description does not explain the return format or error handling. While the tool is straightforward, the lack of output details may leave an AI agent uncertain about the response structure. It is adequate but not fully complete.
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 100%, so baseline is 3. The description adds minimal value beyond schema: it mentions auto-limiting and default max_rows, but these are already implied by the schema descriptions. No further parameter-level details (e.g., format, restrictions) are provided.
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?
Description explicitly states '执行只读 SELECT 查询' (execute read-only SELECT query), clearly defining the tool's purpose as a read-only data query tool. It distinguishes itself from sibling tools (e.g., list_tables, get_table_schema) by focusing on data retrieval via SELECT statements.
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?
Description provides specific usage guidance: use Oracle dialect (NVL, TO_CHAR, etc.) and notes the database is OceanBase Oracle mode. It implicitly advises against using MySQL syntax. However, it does not explicitly state when not to use this tool versus alternatives, such as for DDL or schema exploration.
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?
Discloses that the token becomes invalid after use and cannot be reused. However, does not detail what the write operation entails or potential side effects. Given no annotations, this is reasonable.
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 sentences, front-loaded with purpose, no fluff. Every sentence is meaningful and concise.
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 simple single-parameter tool with no output schema, description covers workflow step and token behavior. Could mention expected outcome (e.g., success/failure), but overall sufficient.
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%, and the schema already describes token as a one-time token from preview_write. The description adds workflow context but does not add significant new semantics beyond the schema.
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?
Description clearly states it is the second step of a two-phase confirmation, confirming and executing a write operation. It specifies the required input (token from preview_write) and distinguishes itself from sibling tools like preview_write.
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?
Explicitly states when to use (after preview_write) and that the token is one-time use. Does not explicitly mention when not to use, but the two-step process context is clear enough.
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 full burden. It clearly states non-destructive behavior (preview only), lists acceptable statement types, and outlines the required user confirmation step. Could add token expiry or error handling 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 concise and front-loaded with key information: purpose, operations, output, non-execution guarantee, and LLM instructions. No redundant sentences.
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 simple tool (1 param, no output schema), the description covers essential aspects: what it does, its output, and its place in the workflow with confirm_write. Lacks details on token expiry or response format but is sufficient for basic usage.
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 coverage is 100% with a brief description. The tool description adds value by listing the specific statement types accepted and explaining the preview purpose, going beyond the schema's generic wording.
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 previews write operations (INSERT, UPDATE, DELETE, CREATE, ALTER, DROP, TRUNCATE) and returns preview info and a token. It explicitly says it does not execute writes, distinguishing it from confirm_write and other siblings.
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 explains this is step 1 of a two-phase confirmation, requiring user confirmation before proceeding to confirm_write. It implicitly distinguishes from read-only tools and provides clear context, though no explicit 'do not use if' guidance.
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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