Skip to main content
Glama
PSPDFKit

Nutrient PDF MCP Server

Official
by PSPDFKit
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: one retrieves the entire object tree structure, the other resolves a specific indirect object by ID. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with underscore: 'get_pdf_object_tree' and 'resolve_indirect_object'. The naming is uniform and predictable.

    Tool Count3/5

    With only 2 tools, the surface is minimal but appropriate for the narrow domain of PDF object tree inspection. It's borderline, but each tool serves a specific, complementary purpose.

    Completeness3/5

    For the stated purpose of exploring a PDF object tree, the tools provide tree retrieval and resolution of indirect objects. However, missing operations like filtering objects by type or searching objects limit completeness for more advanced use cases.

  • Average 2.9/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • 0 of 1 community issues answered or closed 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

  • 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

  • Behavior1/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description should disclose behavioral traits such as whether the tool is read-only, has side effects, or requires specific permissions. It simply states 'resolve', which is vague, and fails to mention any limitations or consequences, making it insufficient for safe usage.

    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 sentence without unnecessary details, but it includes the preamble 'Nutrient PDF MCP Server -', which adds slight noise. It is concise overall, but could be more direct by removing the prefix.

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

    Completeness1/5

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

    The description is extremely brief and does not explain what an indirect object is, how resolution works, the expected output, or any error handling. Given the lack of annotations and output schema, this is far from complete enough for an agent to use the tool confidently.

    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 schema already describes all parameters with 100% coverage, so the description adds no additional semantic value beyond the schema. The baseline of 3 is appropriate as the description does not clarify the meaning of parameters like 'depth' or how to use them effectively.

    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 (resolve) and the resource (indirect object by its ID), making it easy to understand the tool's purpose. However, it does not explicitly differentiate from the sibling tool 'get_pdf_object_tree', which might also involve object retrieval, but the focus on a specific object by ID is implied.

    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 'get_pdf_object_tree'. There is no mention of context, prerequisites, or when not to use it, leaving the agent without decision support.

    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 mentions lazy loading, which is a key behavioral trait. However, with no annotations provided, the description carries the full burden. It does not state whether the tool is read-only, what happens if the PDF is malformed, or provide any concurrency or rate limit information. The lazy loading mention is a minimal but useful disclosure.

    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 with no wasted words. It front-loads the key purpose ('Get JSON representation of PDF object tree') and includes the server context. It is appropriately short for a simple tool.

    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 medium complexity (4 parameters, one sibling tool) and no output schema, the description is minimally complete. It covers the basic purpose and lazy loading but lacks return value expectations, error handling hints, or usage scenarios. The sibling tool is not explained, leaving the agent to guess when to use each.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 100%, so the baseline is 3. The description does not add any meaning beyond what the schema already provides; it simply restates the overall purpose. The schema descriptions for each parameter are clear and complete, so the description's lack of additional parameter context is acceptable.

    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 states 'Get JSON representation of PDF object tree' which is a clear verb and resource. It distinguishes from the sibling tool 'resolve_indirect_object' implicitly by focusing on the full tree rather than indirect object resolution. The mention of lazy loading adds specificity.

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

    Usage Guidelines2/5

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

    No guidance on when to use this tool versus its sibling 'resolve_indirect_object'. There is no mention of prerequisites, limitations, or alternative tools for different use cases. The description does not help the agent decide between this and related tools.

    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

nutrient-pdf-mcp-server MCP server

Copy to your README.md:

Score Badge

nutrient-pdf-mcp-server 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/PSPDFKit/nutrient-pdf-mcp-server'

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