Skip to main content
Glama
fabian1710

MCP Intercom Server

by fabian1710
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation4/5

    The two tools have distinct primary purposes: one fetches recent conversations by time window, while the other searches with multiple filters. However, there is some overlap since 'list-conversations-from-last-week' could be seen as a subset of what 'search-conversations' can do with a created_at filter, which might cause minor confusion.

    Naming Consistency4/5

    Both tools use kebab-case and follow a verb-noun pattern (list-conversations, search-conversations), which is consistent. The addition of 'from-last-week' in the first tool name is descriptive but breaks the pure verb-noun convention slightly, though it remains readable.

    Tool Count2/5

    With only 2 tools, the server feels under-scoped for an Intercom integration. It lacks essential operations like creating, updating, or replying to conversations, which are core to customer support workflows, making the toolset too thin for the apparent domain.

    Completeness2/5

    The server only provides read-only access to conversations, missing critical CRUD operations such as creating conversations, sending messages, updating conversation states, or managing users. This leaves significant gaps that will hinder agents from performing common Intercom tasks.

  • Average 3.2/5 across 2 of 2 tools scored.

    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
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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 the full burden of behavioral disclosure. It mentions the action ('fetch') but doesn't specify whether this is a read-only operation, if it requires authentication, how results are returned (e.g., pagination, format), or any rate limits. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, efficient sentence that front-loads the core functionality ('fetch all conversations from the last week') with a clarifying parenthetical ('last 7 days'). There is zero wasted text, making it highly concise and well-structured for quick understanding.

    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 (0 parameters, no output schema, no annotations), the description is adequate as a minimum viable explanation. It covers the basic purpose but lacks details on behavioral traits and usage context, which are needed for full completeness, especially with a sibling tool available. This results in a baseline score of 3.

    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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't add parameter details, and the baseline for 0 parameters is 4, as it avoids unnecessary repetition while being clear about the tool's scope (time-based fetching).

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the verb ('fetch') and resource ('conversations') with a specific time constraint ('from the last week (last 7 days)'), making the purpose unambiguous. However, it doesn't explicitly differentiate from the sibling tool 'search-conversations', which likely offers more flexible filtering options, preventing a perfect score.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus the sibling 'search-conversations', nor does it mention any prerequisites, exclusions, or alternative scenarios. It simply states what the tool does without contextual usage advice, leaving the agent to infer when this specific time-bound fetch 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 the full burden of behavioral disclosure. It mentions the search capability and filter parameters but doesn't describe what the search returns (e.g., format, pagination), rate limits, authentication needs, or potential side effects. This leaves significant gaps for a search tool with 6 parameters.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, efficient sentence that front-loads the core purpose ('Search Intercom conversations') followed by specific filter details. Every word contributes value with no wasted text, making it highly concise and well-structured.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the complexity (6 parameters, nested objects, no output schema, and no annotations), the description is incomplete. It doesn't explain the return format, pagination, error handling, or how multiple filters interact. For a search tool with this level of detail in the schema, the description should provide more contextual guidance to compensate for missing structured data.

    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 67%, and the description lists the filterable fields (created_at, updated_at, source type, state, open, read), which aligns with the 6 parameters in the schema. However, it doesn't add meaningful semantic context beyond what the schema already provides (e.g., explaining how filters combine or providing examples), so it meets the baseline for moderate coverage.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('Search Intercom conversations') and resource ('conversations'), making the purpose immediately understandable. It distinguishes from the sibling 'list-conversations-from-last-week' by specifying it's a search with filters rather than a time-limited list, though it doesn't explicitly name the alternative.

    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 context by listing specific filterable attributes (created_at, updated_at, source type, state, open, read), suggesting when to use this tool for filtered searches. However, it doesn't explicitly state when to choose this over 'list-conversations-from-last-week' or provide any exclusions or prerequisites.

    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

mcp-intercom MCP server

Copy to your README.md:

Score Badge

mcp-intercom MCP server

Copy to your README.md:

Latest Blog Posts

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/fabian1710/mcp-intercom'

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