onec-odata-mcp

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
All eight tools have clearly distinct purposes: building query parameters, counting records, explaining entity fields, summarizing financial entities, listing entities, retrieving metadata, checking configuration, and executing queries. There is no overlap or ambiguity between tools.
Naming Consistency3/5
All tools use the consistent 'odata_' prefix in snake_case, but the second part mixes verbs (build, count, explain, list, query) and nouns (config, financial_summary, metadata). A fully consistent pattern would use verbs consistently (e.g., odata_get_config, odata_get_metadata).
Tool Count5/5
With 8 tools, the set is well-scoped for interacting with 1C OData services. It covers listing, querying, metadata retrieval, configuration check, and specialized financial summarization without being bloated or too sparse.
Completeness4/5
The set covers core read operations (list, query, count, metadata, config) and adds a specialized financial summary. Missing are create/update/delete operations, but these may be out of scope. A minor gap is the lack of a direct 'get entity by ID' tool, though query with filter can achieve this.
Average 3.5/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 13 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 failing
This repository is licensed under MIT License.
This repository includes a README.md 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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description lacks any disclosure of side effects, authentication needs, or safety profile (e.g., read-only nature is implied but not stated). The tool's behavioral impact is opaque.
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?
The description is a single concise sentence of 10 words, front-loading the purpose. It avoids redundancy but could benefit from a slightly more detailed structure.
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 tool with one parameter and no output schema, the description covers core functionality. However, it omits important context like expected return format (e.g., text or structured data) and whether the description is generated from OData 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 coverage is 100% with a meaningful description for the single parameter. The tool description adds no extra semantic value beyond what the schema provides, but the schema itself is adequate.
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 uses a specific verb ('describe') and resource ('entity fields, types, keys and navigation properties from metadata'), clearly distinguishing it from sibling tools like odata_query (data retrieval) and odata_list_entities (listing entities).
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 vs alternatives (e.g., odata_metadata or odata_query). It does not mention prerequisites or contexts where it is appropriate.
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 transparency. It only states it lists entities but does not disclose whether it is read-only, requires authentication, or has any side effects. For a simple read operation, more detail would be helpful.
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 with no unnecessary words. It is appropriately concise and front-loaded with the key action and resource.
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 that there is no output schema and many sibling tools exist, the description is too minimal. It does not explain what information the list contains (e.g., names, metadata), how to use the results, or any performance considerations. More context is needed to fully guide the agent.
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 no parameters, so the description does not need to explain parameter semantics. Baseline score of 4 is appropriate as the description is sufficient for a parameterless 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 that the tool lists all available OData entities from 1С. It uses a specific verb ('List') and resource ('OData entities'), and it distinguishes itself from sibling tools like odata_query or odata_build_query which are for querying data.
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 one wants to discover available entities, but it lacks explicit guidance on when to use it vs alternatives, such as odata_metadata or odata_explain_entity. No when-not or prerequisites are mentioned.
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?
The description indicates a safe read operation ('Count records'), but with no annotations, it does not disclose potential constraints like pagination, timeouts, or the exact return format, leaving some behavioral aspects unclear.
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?
The description is extremely concise at one sentence, with no wasted words. It is front-loaded with the core action, but it could benefit from a bit more detail without becoming verbose.
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's simplicity (2 parameters, no output schema), the description minimally covers the action. However, it fails to specify the return value (e.g., a count number) or any error conditions, which could be important 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 coverage is 100%, so the description adds little beyond what the schema already provides ('Entity name' and 'OData $filter expression'). The description reiterates 'optionally with filter' but no new semantic detail.
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 'Count' and the resource 'records in an OData entity', with the optional filter distinguishing it from sibling tools like odata_query which retrieves 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?
No explicit guidance on when to use this tool versus alternatives like odata_query for retrieving records or odata_build_query for complex queries. The description lacks context for optimal 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?
No annotations provided, so description must cover behavioral traits. It does not mention authentication, rate limits, read-only nature, error handling, or result structure. Only states query capability without safety or side-effect 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?
Single clear sentence plus an informative example. Every word serves a purpose, and the key parameters are front-loaded. 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?
No output schema and no description of return format. While the tool is simple, a missing output schema and lack of behavior details leave some gaps. Sibling tools exist but no indication when to use this over others.
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 good descriptions for each parameter. The description adds the example but doesn't significantly enhance semantics beyond what the schema already provides.
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?
Clearly states it queries 1С OData entities with specific parameters (filter, select, top, orderBy, expand) and provides a concrete example. Distinguishes from siblings like odata_build_query (building queries?) by focusing on execution.
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 an example but no explicit guidance on when to use this tool versus siblings (e.g., odata_build_query, odata_list_entities). The purpose is clear but selection criteria are absent.
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 description must disclose behavior. It mentions caching with TTL 1h and a refresh parameter, which provides insight into data freshness. However, it lacks details on what happens during refresh 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with clear verb and resource, no unnecessary words.
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?
The tool has one parameter and no output schema. Description covers purpose and caching behavior but does not detail the output format (what entity types and sets look like). Adequate for a simple tool, but could be more complete.
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 schema has 100% coverage with one parameter 'refresh' described as 'Force refresh metadata cache'. The tool description adds context about caching and TTL, enhancing understanding of the parameter's effect.
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 the resource 'entity types and entity sets from 1С metadata'. It distinguishes from sibling tools which focus on querying, counting, etc., though not explicitly.
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 retrieving metadata with caching, but does not specify when to use this instead of siblings like odata_explain_entity. No explicit alternatives or when-not-to-use.
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 disclose behavioral traits. It only indicates a read-like operation (check) but does not mention side effects, authentication needs, response format, 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, front-loaded sentence that conveys the core purpose efficiently with no wasted words.
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 parameters, output schema, or annotations, the description covers the basic purpose but lacks details on what the tool returns (e.g., status string, instructions in text).
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 0 parameters, and schema coverage is 100% trivially. The description does not need to add parameter meaning beyond the schema, which is already 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 clearly states the verb 'Check' and the resource '1С OData configuration', distinguishing it from sibling tools like odata_query (querying) and odata_metadata (metadata).
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 checking configuration status or getting setup instructions, but provides no explicit guidance on when to use it vs. alternatives or any 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 description carries burden. It describes a build operation with no side effects, which is reasonable, but doesn't explicitly state safety or lack of mutations.
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 action and output, no unnecessary words.
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 7 parameters with nested objects and no output schema, the description is too brief. Missing details on return format, validation behavior, error handling, and examples.
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 (29%), and description only broadly mentions parameter categories (filters, date period, sort, top). It adds some grouping but lacks detailed semantics for individual parameters.
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 builds validated OData query parameters from structured intent and returns them for use with odata_query. It distinguishes itself from odata_query (execution) by being a preparatory step.
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 implies it should be used before odata_query. It doesn't explicitly state when not to use or alternatives, but the context is clear.
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, the description must disclose behavioral traits. It only states auto-detection and counting, not whether it's read-only, if it triggers data scans, or any performance implications. This is minimal 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?
A single, clear sentence with no redundant information. It is well front-loaded with the key purpose.
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 parameters and no output schema, the description is reasonably complete for a simple summary tool. However, it does not explain the format or scope of counts.
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?
No parameters exist, so baseline is 4. The description adds value by specifying which entities are detected, providing context beyond the empty 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?
The description explicitly states the tool auto-detects common 1С financial entities (счета, реализации, контрагенты, заявки) and shows counts. It clearly distinguishes itself from sibling tools like odata_query or odata_count by specifying its specialized function.
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 obtaining quick counts of financial entities, but provides no explicit guidance on when to use this vs. alternatives like odata_query or odata_count. It lacks context for 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.

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