ms-graph-dev

Server Quality Checklist

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

Latest release: v0.1.11
Disambiguation5/5
Each tool has a clearly distinct purpose: listing categories, loading them, searching API, and various explanatory tools cover different aspects (pagination, OData, throttling, delta, batch, permissions, errors). No two tools overlap in functionality.
Naming Consistency5/5
Tool names follow a consistent verb_prefix pattern: list_, load_, search_, graph_explain_, graph_build_. The convention is uniform and predictable across all 11 tools.
Tool Count5/5
11 tools is well-scoped for a developer assistant covering Microsoft Graph. It provides both utility (category management, search) and educational content (explanations) without being overwhelming.
Completeness2/5
The tool set lacks actual API execution tools like 'list_users' or 'send_mail'. While explanations are helpful, the server cannot perform real Graph operations, leaving significant gaps for an agent trying to interact with Microsoft Graph.
Average 4/5 across 11 of 11 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on April 18, 2026
- 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?
No annotations are provided, so the description must fully disclose behavioral traits. It states 'Build a Microsoft Graph JSON batch request body' but does not clarify whether the tool only builds the body or also sends the request. It lacks information about side effects, required permissions, or rate limits.
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 efficiently conveys the core purpose without extraneous 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?
Despite the tool's simplicity (1 parameter), the description lacks details about the return value (e.g., does it return a string or object?), does not explain output format, and provides no usage context. Without an output schema, more description is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no parameter-level information, and schema description coverage is 0%. Although the input schema includes property descriptions, the tool's text does not compensate, missing an opportunity to clarify parameter usage or constraints 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?
The description 'Build a Microsoft Graph JSON batch request body — combine up to 20 API calls into a single POST to /$batch' uses a specific verb (Build), identifies the resource (JSON batch request body), and distinguishes it from sibling tools like graph_explain_batch by indicating it constructs the actual request.
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 you need to batch multiple Graph API calls, but it does not explicitly state when to use this tool versus alternatives like graph_explain_batch, nor does it provide exclusion criteria or prerequisites (e.g., authentication).
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 convey behavioral traits. It fails to state whether the operation is read-only, what authentication is required, or the format of results. This is a significant gap for an API search tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with one sentence for the main purpose followed by a clear list of covered areas. It is front-loaded and contains 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?
Given the tool's complexity (searching many Graph APIs) and lack of output schema, the description omits details about the output format (e.g., list of endpoints, documentation snippets) and pagination. It covers scope well but lacks output context.
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% for the single 'query' parameter. The description adds example terms but no extra syntax or constraints beyond what the schema provides, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search Microsoft Graph REST API endpoints by keyword.' It enumerates covered services (SharePoint, OneDrive, Exchange, etc.), distinguishing it from sibling tools that explain concepts like pagination or throttling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context by listing covered domains and example queries, implying the tool is used to discover API endpoints for a given task. However, it lacks explicit guidance on when not to use this tool or prerequisites (e.g., authentication).
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?
As an explanatory tool with no annotations, the description minimally implies no side effects but doesn't explicitly state it's read-only or provide behavioral details. Acceptable for a non-action tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single, front-loaded sentence conveying key information efficiently.
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 the tool's low complexity and lack of output schema, the description fully covers its purpose and functionality.
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, per instructions baseline is 4. Description adds no parameter info but schema coverage is 100%.
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 explains Microsoft Graph JSON batching and its benefit of reducing round trips. It clearly distinguishes from sibling tools like graph_explain_pagination.
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 graph_explain_throttling or graph_explain_permissions. While purpose is clear, no context for usage decisions.
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 carries full burden for behavioral transparency. It reveals that the tool activates tools, can be called early, and supports multiple categories. However, it does not disclose whether the operation is idempotent, what side effects occur (e.g., resetting previous loads), or expected return behavior (e.g., success confirmation).
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 at 3 sentences. It front-loads the primary action, then usage timing, then category list, and ends with multi-load capability. 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?
Given the absence of annotations and output schema, the description covers core purpose, usage, and categories but omits expected return value or permission requirements. For a simple 1-parameter tool, this 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.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (parameter described). The description adds semantic context by listing each category and its associated Microsoft services (e.g., 'users' for Azure AD/Entra ID). This enriches the bare schema description beyond the baseline of 3.
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: 'Activate Graph API tools for a resource category.' This specific verb+resource combination is distinct from sibling tools like list_categories or graph_explain_*, making it easy for an AI agent to select correctly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Call this as soon as the user's intent is clear — do not wait for confirmation.' It also notes that multiple categories can be loaded for tasks spanning areas. However, it does not explicitly contrast with list_categories, though the purpose is distinct.
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?
No annotations provided, so description carries burden. It states the action (explain and fix) and scope (specific error codes). Could mention that it returns textual guidance, but behavior is clear for a read-only explanation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that efficiently conveys purpose and scope. 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 parameterless explanation tool, description covers key purpose and examples. Lacks explicit mention of output format (e.g., returns text), but overall sufficient given simplicity.
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?
No parameters exist, and schema coverage is 100%. Baseline score applies. Description adds value by listing error codes, but there is no parameter elaboration 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?
Description clearly states the tool explains common Graph API error codes and fixes, listing specific codes. This distinguishes it from sibling explanation tools focusing on other topics (pagination, throttling, etc.).
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?
Implies use when encountering listed error codes, but no explicit guidance on when to use alternatives like graph_explain_throttling or graph_explain_pagination. Lacks 'when not to use' comparisons.
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 are provided, so the description carries the burden. It correctly indicates the tool is explanatory (non-destructive) but lacks details on any specific behavioral traits, such as whether it fetches data or just returns text.
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 action ('Explain') and specific resource ('Microsoft Graph delta queries'). Front-loaded with the key benefit: 'get only changed items since last sync'. No waste.
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?
For a parameterless explanation tool with no output schema, the description fully covers what the tool does. It is complete given the tool's simplicity and context.
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?
Input schema has no parameters, so schema coverage is 100%. With 0 parameters, baseline is 4. Description does not need to add parameter info.
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 explains Microsoft Graph delta queries for efficient change tracking. It distinguishes itself from sibling tools like graph_explain_pagination and graph_explain_odata by focusing on delta queries specifically.
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?
Description implies the tool is for understanding delta queries but does not explicitly state when to use it versus alternatives like graph_explain_pagination or graph_explain_batch. No when-not or alternative guidance is provided.
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?
No annotations are provided, but the description accurately characterizes the tool as a read-only explainer. It does not disclose details like network usage or side effects, which are minimal for an informational tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that effectively communicates the tool's purpose. It is front-loaded with the action verb and includes specific details without any fluff.
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 informational tool with no parameters and no output schema, the description is nearly complete. It could mention the return format (e.g., text explanation), but that is a minor gap.
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, so schema coverage is 100%. The description does not need to add parameter meaning. Baseline score of 4 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 states it explains OData query parameters supported by Microsoft Graph and lists the specific parameters. This distinguishes it from sibling tools like graph_explain_pagination or graph_explain_throttling.
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 provide explicit guidance on when to use this tool versus alternatives. It implies usage for understanding OData parameters but lacks when/not-to-use context.
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?
No annotations are provided, but the description accurately conveys the tool's informational nature and lack of side effects. It transparently describes the content (pagination explanation) without misleading the agent. However, it could mention the return format or that it does not trigger API calls.
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 is clear, direct, and contains no extraneous words. It is front-loaded with the main purpose and is appropriately concise for the tool's simplicity.
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 has no parameters or output schema, the description is sufficient to convey its purpose. However, it does not mention what the explanation looks like (e.g., text, format), which could be helpful but is not critical. The presence of multiple sibling explain tools suggests a bit more context could be beneficial.
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 zero parameters, and the schema coverage is trivially 100%. The description correctly omits parameter details since none exist. It adds no extra semantic information beyond the schema, but none is 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 tool explains Microsoft Graph API pagination with specific detail about @odata.nextLink and iterating results. The verb 'explain' and resource are explicit, and it distinguishes from sibling explain tools by focusing on pagination.
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 only states what the tool does, with no guidance on when to use it versus sibling tools like graph_explain_odata. It does not provide context for choosing this tool over alternatives, limiting its helpfulness for an AI agent deciding which tool to invoke.
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 are provided, and the description does not disclose any behavioral traits beyond its explanatory purpose. It is a stateless knowledge tool, so side effects are not expected, but the description lacks details like whether it makes network calls or returns cached data.
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 front-loads the key topic (permissions) and enumerates subtopics. No wasted words.
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 parameters and no output schema, the description is sufficiently complete. It covers the main topics (delegated vs application, app registration, least-privilege) and is consistent with sibling explanation tools.
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 the description covers the purpose fully. Baseline of 4 applies as no parameters require additional 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?
The description clearly states it explains Microsoft Graph permission types, including delegated vs application, app registration, and least-privilege best practices. It distinguishes itself from siblings like graph_explain_pagination and graph_explain_throttling by focusing specifically on permissions.
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 implies usage for understanding permission concepts, but does not explicitly state when to use this over other explanation tools like graph_explain_odata. However, the topic is sufficiently distinct from siblings, making the context clear.
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 fully conveys the tool's purpose as an informational explainer. It explicitly lists what it covers (causes, handling, limits), and while it doesn't state it has no side effects, its nature as a read-only explanation is clear.
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 immediately conveys the tool's purpose. No redundant information or filler. Every word earns its place.
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 the tool's simplicity (no parameters, no output schema), the description is fully complete. It covers what the tool does and what topics it explains, providing all necessary context for an agent to select and invoke it correctly.
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 no parameters, and the schema coverage is 100% (empty). The description adds significant value by explaining the tool's purpose without needing to describe parameters. Baseline is 4 for zero-parameter tools.
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 explains Microsoft Graph throttling, including causes of 429 errors, handling Retry-After, and service-specific limits. It uses a specific verb-resource pair and is easily distinguishable from sibling tools like graph_explain_errors or graph_explain_pagination.
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 understanding throttling issues but does not explicitly state when to use this tool versus alternatives. It lacks guidance on exclusions or specific scenarios where other explain tools would be more appropriate.
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?
The description clearly states what the tool returns (categories, descriptions, load status). No annotations are provided, but the description sufficiently discloses the read-only nature of this tool. No hidden behaviors are implied.
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 one sentence plus a concise list. It front-loads the main purpose and adds value with example categories. Every element earns its place without verbosity.
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 the tool's simplicity (zero parameters, no output schema), the description fully explains what the tool returns and its purpose. No annotations are needed as the description provides sufficient behavioral context.
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, so the schema coverage is 100%. Per guidelines, baseline is 4 for 0-parameter tools. The description does not need to add parameter details.
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 Microsoft Graph API resource categories with descriptions and load status, and enumerates examples. It distinguishes itself from sibling tools that focus on loading a specific category or other Graph API operations.
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 discovering categories before using tools like load_category, but does not explicitly state when to use this tool instead of alternatives or provide exclusions. No guidance on prerequisites or context.
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:

[![ms-graph-dev-mcp MCP server](https://glama.ai/mcp/servers/stubbedev/ms-graph-dev-mcp/badges/card.svg)](https://glama.ai/mcp/servers/stubbedev/ms-graph-dev-mcp)

Score Badge

Copy to your README.md:

[![ms-graph-dev-mcp MCP server](https://glama.ai/mcp/servers/stubbedev/ms-graph-dev-mcp/badges/score.svg)](https://glama.ai/mcp/servers/stubbedev/ms-graph-dev-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/stubbedev/ms-graph-dev-mcp'

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