openemis-mcp

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.2.0
Disambiguation5/5
Each tool has a clearly distinct purpose: health check, generic data fetch, domain listing, topic discovery, playbook listing, and playbook details. There is no overlap or ambiguity.
Naming Consistency5/5
All tools follow the consistent 'openemis_' prefix with snake_case names (health, get, list_domains, discover, list_playbooks, get_playbook), providing a clear and predictable pattern.
Tool Count5/5
With 6 tools covering health, data retrieval, domain exploration, and playbook access, the count is well-scoped for a focused integration without being too sparse or bloated.
Completeness4/5
The tool set provides comprehensive read access via openemis_get and discovery tools, but lacks write operations (create, update, delete). For a read-oriented server this is acceptable, though adding CRUD would improve completeness.
Average 4/5 across 6 of 6 tools scored. Lowest: 3.2/5.
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 is passing
This repository is licensed under MIT License.
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.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 full burden for behavioral disclosure. It only states the action ('get') implying read-only, but omits any details about permissions, rate limits, or side effects. No safety or behavioral traits are mentioned.
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, clear sentence that is front-loaded with the action and resource. It is concise without wasted words, though it could be slightly more structured (e.g., bullet points for what is included).
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 adequately conveys the purpose and key input. However, it does not describe the return format or error handling, which would be helpful for 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?
The input schema has 100% description coverage for the single 'id' parameter, including an example. The description reinforces the parameter's role ('by playbook id') but adds no additional meaning or constraints beyond what the schema provides. Baseline 3 is appropriate.
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 the full playbook details') and resource ('steps and coverage by playbook id'). It specifies what is included, distinguishing it from sibling tools like openemis_list_playbooks which lists playbooks without details. However, it does not explicitly differentiate from openemis_get.
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 a playbook id is available and full details are needed, but it provides no explicit guidance on when not to use this tool or alternatives. Sibling tool differentiation is implicit through context, 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, the description must fully disclose behavioral traits. It only states the search scope (name, summary, description) but omits details like whether matches are partial/fuzzy, result limits, authentication requirements, or any side effects. For a search tool, basic behavioral context is missing.
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 composed of two succinct sentences. The first presents the primary function, and the second details the search scope. Every word adds value, with no redundancy or superfluous content.
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 simplicity (one parameter, no output schema) and the presence of sibling tools for specific retrievals, the description adequately defines the tool's role as a cross-resource search. However, it could improve by mentioning the return format (e.g., list of matches) or any limitations.
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 meaning by explaining that the 'topic' parameter is matched against names, summaries, and descriptions of domains, families, and playbooks. This goes beyond the schema's example list to clarify the search scope.
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 'discover' and specifies the resources (endpoints, playbooks, domains, families) and search fields (name, summary, description). This distinguishes it from sibling tools like openemis_list_domains and openemis_get, which are likely for listing or retrieving specific items.
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 does not provide explicit guidance on when to use this tool versus its siblings. It lacks statements like 'Use this to search across multiple resource types; use openemis_get to retrieve a specific playbook by ID.' No exclusions or alternatives are mentioned.
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 disclosing behavioral traits. The description does not mention whether the operation is read-only (likely), idempotent, or has any side effects. It also does not discuss authentication, rate limits, or data freshness. Given no annotations, this is a significant gap.
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, well-structured sentence that efficiently conveys the purpose, output content, and a hint for further action. Every part serves a purpose 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?
The tool has no parameters, no output schema, and no annotations. The description provides enough context about what it returns (list of domains with summaries and endpoint counts) and hints at the next step. It is complete for a simple list tool, though it could optionally describe the return format (e.g., array vs object) for added clarity.
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 input schema has no parameters, and schema coverage is 100%. With zero parameters, the baseline is 4, and the description does not need to add parameter information. The description is adequate for a parameterless tool.
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 lists all available domains with summaries and endpoint counts, and it hints at further exploration via openemis_discover. It distinguishes itself from siblings like openemis_get (retrieving a specific resource) and openemis_list_playbooks (listing playbooks) by specifying the resource type (domains) and the information provided (summaries, counts).
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 does not explicitly state when to use this tool versus alternatives. It suggests exploring via openemis_discover after listing domains, but it lacks explicit exclusions or comparisons to sibling tools like openemis_health. The guidance is minimal and implied rather than direct.
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; the description indicates a read-only listing operation with no side effects. However, it lacks details on performance, rate limits, or scope (e.g., whether the list is exhaustive or paginated).
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, front-loaded with the verb and resource, no redundant words. Efficient and clear.
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 fields returned (id, title, audience, domain), which is helpful. However, it omits any information on ordering, pagination, or error handling, which would enhance completeness.
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 0 parameters, and schema description coverage is 100% (empty schema). Per guidelines, baseline is 4; no additional parameter info needed.
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 action ('List all available playbooks') and the returned fields (id, title, audience, domain). It distinguishes itself from sibling tools like openemis_get_playbook (for a single playbook) and openemis_list_domains (different resource).
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 use when a list of all playbooks is needed but does not explicitly state when to use alternatives or when not to use it. No guidance on prerequisites or complementary tools.
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?
Without annotations, the description adds value by specifying a 'real login round-trip,' indicating it is more than a simple ping. However, it does not detail side effects or required permissions.
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, 16 words, front-loaded with key action. 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?
For a zero-parameter health check with no output schema, the description covers the main purpose well. It could mention return value format, but is otherwise sufficient.
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 schema coverage is 100%. Description does not need to add param info, baseline 4 applies.
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 checks endpoint reachability and credential validity with a real login round-trip. This distinguishes it from sibling tools that retrieve data or list domains.
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 verifying connectivity before other operations, but lacks explicit when-to-use or when-not-to-use guidance, and does not reference sibling tools.
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?
Discloses response format ({safety, data}), HTTP 400 for non-existent fields, support for pagination, and security risks. Since no annotations exist, description fully informs about tool behavior.
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 long but well-structured with clear sections. Every sentence adds useful information, though some redundancy could be trimmed for conciseness.
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 no output schema, the description adequately covers return format. It addresses all parameter use cases, edge conditions, and security. Sibling tools are distinct, so no missing context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Adds significant value beyond the schema: details _conditions syntax with examples (exact, wildcard, comparison, value list), explains ids parameter, and clarifies that bare field params should not be used. All 3 parameters are documented in schema (100% 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 'Fetch data from an OpenEMIS v5 resource' and distinguishes behaviors for single record vs list, and batch-fetching. It differentiates from sibling tools (e.g., openemis_health, openemis_discover) by focusing on data retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides extensive guidance: when to use id vs ids vs _conditions, limitations, and explicit advice to never loop with individual calls. Also warns about proper filtering syntax and prompt injection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![openemis-mcp MCP server](https://glama.ai/mcp/servers/tixuz/openemis-mcp/badges/card.svg)](https://glama.ai/mcp/servers/tixuz/openemis-mcp)

Score Badge

Copy to your README.md:

[![openemis-mcp MCP server](https://glama.ai/mcp/servers/tixuz/openemis-mcp/badges/score.svg)](https://glama.ai/mcp/servers/tixuz/openemis-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/tixuz/openemis-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server