PDF Kit
Server Details
AI-powered PDF tools: fill forms via natural language
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 2.9/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is clearly defined and distinct by default.
The single tool name follows a consistent verb_noun pattern (create_autofill_job), and there are no other tools to create inconsistency. The naming is straightforward and predictable.
A single tool is too few for a server named 'PDF Kit', which implies a broader set of PDF-related functionalities. This feels thin and under-scoped for the apparent domain.
The server name suggests a toolkit for PDF operations, but only one tool for creating autofill jobs is provided. There are significant gaps, such as tools for reading, editing, merging, or converting PDFs, which are typical for PDF toolkits.
Available Tools
1 toolbasestation.create_autofill_jobCInspect
Create a PDF form filling job using AI to automatically fill out forms based on natural language instructions.
| Name | Required | Description | Default |
|---|---|---|---|
| formFile | Yes | Base64-encoded PDF file to fill | |
| callbackUrl | No | Optional callback URL for job completion notification | |
| instructions | Yes | Natural language instructions for how to fill the form |
Tool Definition Quality
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 states the tool creates a job using AI but does not cover critical aspects like permissions required, whether it's idempotent, rate limits, error handling, or what happens after job creation (e.g., asynchronous processing). This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the core functionality without unnecessary details. It is front-loaded with the main action and uses clear language, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of an AI-based job creation tool with no annotations and no output schema, the description is insufficient. It lacks information on what the tool returns (e.g., job ID, status), error conditions, or operational details like processing time or callback behavior, leaving the agent with incomplete context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents all parameters thoroughly. The description adds no additional semantic context beyond implying that 'instructions' are natural language and 'formFile' is a PDF, which is already covered in the schema. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Create a PDF form filling job using AI to automatically fill out forms based on natural language instructions.' It specifies the verb ('Create'), resource ('PDF form filling job'), and method ('using AI'), making it easy to understand. However, with no sibling tools provided, it cannot demonstrate differentiation from alternatives, 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.
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, prerequisites, or constraints. It mentions the tool's function but lacks context such as typical use cases, limitations, or comparisons to other methods, leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!