pactlio-mcp

Official

by Pactlio-ai

Overview Schema Related Servers Score Discussions

JavaScript

Remote

Server Quality Checklist

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

Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct aspect of contract drafting and analysis: analyzing existing contracts, checking non-compete enforceability, managing drafts (start, status, checkout), and retrieving reference data (intake questions, jurisdiction requirements, contract types). No overlap or ambiguity.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (e.g., analyze_contract, get_draft_status, start_contract_draft). The naming is predictable and uses snake_case uniformly, making it easy for agents to understand the action and target.
Tool Count5/5
With 8 tools, the server is well-scoped for its purpose of contract drafting and legal analysis. Each tool serves a clear function without redundancy, and the count is neither too sparse nor overwhelming.
Completeness4/5
The tool set covers the core workflow (list types, get intake questions, start draft, check status, get checkout link) and adds analytical capabilities. However, there is no tool to retrieve the full contract text after payment, which may require manual intervention, slightly limiting end-to-end automation.
Average 4.2/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 8 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.
This repository includes a glama.json configuration file.
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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that results include enforceability status, salary thresholds, and key statute for all 50 states + DC. However, it does not specify return format or data recency, leaving some 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 two concise sentences, each serving a purpose: stating what the tool does and its geographic coverage. 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 simple single-parameter tool with no output schema, the description adequately covers what the user gets (enforceability, thresholds, statute) and scope (all US states + DC). Could optionally mention output structure but not essential.
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% and the parameter description in schema is clear. The tool description adds no additional semantic value beyond what the schema already provides.
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 checks enforceability, limited, or void status for non-compete agreements in a US state, including salary thresholds and key statute. It distinguishes itself from siblings like 'analyze_contract' which is broader.
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 checking non-competes in US states, but lacks explicit when-to-use or alternatives. No guidance on when not to use or comparison with sibling tools.
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 must disclose behavioral traits. It describes the return content but does not explicitly state that the tool is read-only, idempotent, or has no side effects. For a query tool, this lack of explicit safety statement is a minor gap.
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 two sentences long, front-loaded with the purpose, and contains no redundant information. Every word contributes to clarity.
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?
There is no output schema, but the description compensates by listing the types of information returned (legal requirements, key differences, etc.). It also provides practical examples for both parameters. Some might wish for more precise return format, but it is largely complete for a query tool.
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% with both parameters clearly defined. The description reinforces the parameters with examples (e.g., 'contractor-agreement', 'california'), but adds no new semantic meaning beyond the schema. Baseline 3 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 the tool retrieves statute-cited legal requirements, key differences, common pitfalls, terminology, and FAQs for a contract type in a specific jurisdiction. It uses a specific verb ('get' implied) and resource, and is distinct from sibling tools like analyze_contract and check_non_compete_enforceability.
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 provides usage format (contract slug + jurisdiction slug) but does not explicitly state when to use this tool versus alternatives, or when not to use it. There is no guidance on exclusions or prerequisites.
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, so the description carries full burden. It discloses that the tool returns a URL for human action, requires a preview_id, and is non-destructive. Missing details on authentication or rate limits, but the operation is simple and the description is transparent enough.
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?
Two sentences with no wasted words. The description is front-loaded with the action and immediately provides the purpose and usage instruction.
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 one parameter, no output schema, and simple operation, the description covers the essential: what the tool does, what input it needs, and what to do with the output. It could mention URL validity but is complete enough for the 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% with one parameter already described in the schema. The description does not add extra meaning beyond referencing the preview_id, so baseline 3 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 states the verb 'Returns' and the resource 'URL', specifies the purpose (view preview and pay to unlock contract), and distinguishes from siblings by explicitly referencing start_contract_draft as a prerequisite.
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 after creating a draft with start_contract_draft and instructs to share the link with the user. It does not explicitly state when not to use or list alternatives, but the context is clear among siblings.
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 provides essential behavioral details: it returns 'pending' while generating, and upon completion returns free preview, locked section titles, and a URL for unlocking. This covers the key states and outputs, though it omits error handling, timeouts, or authentication requirements. It is transparent enough for a simple status poll.
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 efficiently packs prerequisite, behavioral states, and return values without redundancy. It is front-loaded with the action and resource, making it easy to scan.
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 tool with no output schema, the description covers what the agent needs: the prerequisite (start_contract_draft), the status response, and the components of the full preview when complete. It could be improved by noting potential error conditions or status values beyond 'pending' and 'complete', but it is satisfactory for common use cases.
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% (the single parameter 'preview_id' is described). The description does not add new semantics beyond the schema's description; it merely restates that the ID comes from start_contract_draft. Therefore, the description does not significantly enhance parameter understanding beyond what the schema already provides.
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: checking the status of a contract draft started with a specific sibling tool (start_contract_draft). It distinguishes itself from siblings like analyze_contract and get_checkout_link by focusing on the draft generation process and what it returns (pending status, free preview, locked sections titles, unlock URL).
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 explicitly mentions when to use this tool ('Check a contract draft started with start_contract_draft'), providing a clear prerequisite. It covers two states (during generation and when complete), which guides usage. However, it does not explicitly state when not to use it or mention alternatives, which would improve the score.
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, so the description carries full burden. It states the analysis is AI-generated (transparent about automation) and explicitly says 'Not legal advice' (important behavioral disclosure). It does not mention data handling or other side effects, but for a read-only analysis tool this is acceptable.
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?
Two well-structured sentences: first explains what the tool does, second adds critical usage notes (free, daily limit, disclaimer). Every sentence is necessary and front-loaded. 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?
Despite no output schema, the description outlines the return content (red flags, one-sided clauses, etc.). Input schema fully documents parameters. The tool is simple with only two parameters, and the description covers purpose, limitations, and disclaimer, making it complete for an AI agent.
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%, leaving little burden on description. However, the description adds value by providing an example for the optional focus parameter ('IP ownership', 'termination terms') and reiterating the character limits (200 to 50,000) for contract_text, aiding correct usage.
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: analyzing contract text for risks and red flags. It lists specific outputs (red flags, one-sided clauses, missing protections, questions to ask) and distinguishes from narrower siblings like check_non_compete_enforceability.
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 mentions a usage limit (2/day) and a disclaimer (not legal advice), but does not explicitly guide when to use this tool versus alternatives like check_non_compete_enforceability or list_contract_types. It implies usage for general contract risk analysis but lacks exclusions.
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 must carry the full burden. It states the tool 'returns fields, types, options, and validation rules', which is expected for a read operation. However, it does not disclose authentication requirements, rate limits, or any hidden side effects. The description is adequate but not exceptional.
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 extremely concise with two sentences. The first sentence immediately states the purpose, and the second adds usage guidance and return content. No unnecessary words; every sentence 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 (2 parameters, no output schema, no nested objects), the description adequately explains its purpose, return values, and relationship to sibling tools. It covers the essentials for an agent to use 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 input schema covers 100% of parameters with clear descriptions. The description adds value by providing example values for 'contract_type' (e.g., 'nda_mutual') and explaining that jurisdiction is an optional filter. This goes beyond the schema, enhancing clarity.
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 retrieves intake questions for a specific contract type, with a specific verb 'Get' and resource 'intake questions'. It explicitly distinguishes from sibling tools like 'list_contract_types' and 'start_contract_draft' by noting these questions are used before drafting.
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 explicitly instructs to 'use these to collect the information needed before calling start_contract_draft', providing clear context on when to use this tool. It implies a prerequisite relationship but does not explicitly state when not to use it, though the guidance is sufficient for typical use.
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, so the description carries the full burden. It discloses that the tool lists types with descriptions and prices, implying a read-only operation. No side effects or unusual behaviors are indicated, which is appropriate for a list 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?
Two sentences, front-loaded with the verb and resource. No unnecessary words; every part adds meaningful context.
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 that the tool has no parameters and no output schema, the description provides sufficient context about the return content (descriptions, use cases, prices). It is complete for this simple listing tool.
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%. The description adds value by explaining what the output contains (descriptions, use cases, prices), which is beyond the schema's empty definition.
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 uses a specific verb ('List') and resource ('all contract and legal document types'), clearly stating what the tool returns (descriptions, use cases, prices). It also distinguishes from siblings by framing it as the starting point to find slug/id for other tools.
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 explicitly states the use case: 'Start here to find the right contract type slug/id for other tools.' This gives clear context for when to use it, though it does not explicitly mention 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.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses multi-agent AI engine, 3-5 minute generation time, immediate preview_id return, free preview only, full contract unlocked via human checkout. Fully informs agent of behavioral traits.
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?
Two sentences, starts with action, covers key details. Could be slightly more structured, but no wasted words. Efficient.
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 complexity (multi-agent AI, preview vs full, polling needed), description covers all essentials: prerequisites, return value, workflow, limitations. No output schema, but explains preview_id and subsequent polling. Complete for agent to use 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?
Schema covers all 3 parameters (100% coverage). Description adds value: specifies that deal_summary fields come from get_intake_questions, provides example format, and notes that contract_type ids are from list_contract_types. Goes beyond schema alone.
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 'Start generating a contract draft' with specific verb and resource. Distinguishes from siblings like get_intake_questions (data collection) and get_draft_status (polling).
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?
Explicitly says to provide deal_summary fields collected via get_intake_questions and mentions contract_type from list_contract_types. Implicitly indicates when to use (after intake, before polling). No explicit when-not, but context is clear.
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:

[![pactlio-mcp MCP server](https://glama.ai/mcp/servers/Pactlio-ai/pactlio-mcp/badges/card.svg)](https://glama.ai/mcp/servers/Pactlio-ai/pactlio-mcp)

Score Badge

Copy to your README.md:

[![pactlio-mcp MCP server](https://glama.ai/mcp/servers/Pactlio-ai/pactlio-mcp/badges/score.svg)](https://glama.ai/mcp/servers/Pactlio-ai/pactlio-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/Pactlio-ai/pactlio-mcp'

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