A
licenseA
qualityA
maintenanceFill any PDF form with AI agents — ML field detection on scans, visual review loop, reusable templates, and native AcroForm fill via justfill.app.
14
MIT
PDF Kit
Server Details
AI-powered PDF tools: fill forms via natural language
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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.
ParametersJSON Schema
| 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 |
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure, but it only states the basic action. It does not reveal that this is job-creation (implying async), likely returns a job ID, or any side effects like queuing or persistence. The phrase 'using AI' is a minimal behavioral trait, but important details such as duration, failure modes, or state are omitted.
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, front-loaded sentence that efficiently conveys the primary action. There is no fluff or redundancy, and it is appropriately sized for a simple tool, though it could incorporate more detail without becoming verbose.
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?
The tool has moderate complexity (3 parameters, no output schema, no annotations). The description omits critical contextual information such as return value, asynchronous behavior, or any prerequisites. The lack of output schema means the description should explain what the caller receives, but it does not, leaving the tool under-specified.
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 has 100% coverage with descriptive parameter text for formFile, callbackUrl, and instructions. The description adds no additional meaning beyond the schema, and the baseline of 3 is appropriate since the schema already documents parameters adequately.
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 tool's function: creating a PDF form filling job via AI with natural language instructions. It names a specific verb ('Create') and resource ('PDF form filling job'), and includes the mechanism ('using AI') and input type ('natural language instructions'). It lacks comparison with siblings, but no siblings are provided, so this is acceptable.
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 is given on when to use this tool vs alternatives, prerequisites, or context. The description only states what it does, leaving the user to infer appropriate usage. There is no mention of exclusions or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
- First observed
basestation.create_autofill_job
Frequently Asked Questions
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/.well-known/glama.jsonon the same origin as the connector, then choose Check HTTP challenge.DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
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{
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The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
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TDQS
B3.1/5.0
Disambiguation5/5
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.
Naming Consistency5/5
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.
Tool Count2/5
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.
Completeness2/5
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.