hypathesis
Server Details
Variable relationships from research papers with causal direction and source traceback.
- 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 4.6/5 across 4 of 4 tools scored.
Each tool has a clearly distinct purpose: starting auth flow, polling auth status, checking paper analysis status, and retrieving results. No overlap.
All tool names follow a consistent verb_noun pattern with underscores (e.g., check_device_auth, get_analysis_status). Uniform style throughout.
4 tools is well-scoped for the narrow domain of device auth and paper analysis retrieval, covering the key steps without excess.
The auth flow and result retrieval are covered, but there is no upload tool for papers, relying on an external upload endpoint. This leaves a workflow gap.
Available Tools
4 toolscheck_device_authCheck Device AuthARead-onlyIdempotentInspect
Check if the user has completed browser sign-in for a device auth request.
Poll this after calling initiate_device_auth. Returns status 'pending'
while waiting, or 'complete' with an api_key when the user has signed in.
Use the returned api_key as the authorization parameter in other tools.
Args:
user_code: The user_code returned by initiate_device_auth.
Returns:
Pending: {"status": "pending"}
Complete: {"status": "complete", "api_key": "hk_..."}
Error: {"error": "..."}| Name | Required | Description | Default |
|---|---|---|---|
| user_code | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark it as read-only and idempotent. The description adds behavioral details: it returns 'pending' vs 'complete' with an api_key, and can return an error. This adds functional context beyond annotations.
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 short, with a clear purpose paragraph followed by structured Args and Returns sections. Every sentence adds value, no fluff.
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?
Despite lack of an explicit output schema, the description details the three possible return objects. It explains the relationship to initiate_device_auth and how to use the returned api_key. For a simple single-parameter tool, this is complete.
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?
The input schema has 0% coverage, but the description includes an 'Args' section explaining user_code as 'the user_code returned by initiate_device_auth.' This adds semantic meaning beyond the schema's type/required fields.
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 'check's user completion of browser sign-in for a device auth request.' It specifies the verb and resource, and distinguishes from siblings like initiate_device_auth (which starts the flow) and unrelated analysis tools.
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?
Explicitly advises to 'Poll this after calling initiate_device_auth,' providing clear usage context. Does not explicitly mention alternatives or when not to use, but context is well-defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_analysis_statusGet Analysis StatusARead-onlyIdempotentInspect
Check the processing status of an uploaded paper.
Poll this tool after uploading a PDF until status is 'Ready' before
calling get_variable_relationships.
Args:
file_id: The file_id returned by the /upload endpoint.
authorization: Optional. API key as 'Bearer hk_...' or 'hk_...'.
Returns:
{
"status": "Processing" | "Ready" | "Empty" | "Ineligible" | "Pending",
"edges_count": int,
"variables_count": int
}| Name | Required | Description | Default |
|---|---|---|---|
| file_id | Yes | ||
| authorization | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds polling behavior and details return values (status enum, edges_count, variables_count), which goes beyond structured data.
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?
Well-organized with Args and Returns sections, concise language, no wasted words.
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?
With simple tool (2 params, annotations present, output schema described), the description fully covers polling workflow and return values, 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.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but description explains file_id as 'returned by the /upload endpoint' and authorization as 'API key' format, adding meaning beyond parameter names.
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 'Check the processing status of an uploaded paper', using a specific verb and resource. It also distinguishes from sibling tool 'get_variable_relationships' by mentioning it as a subsequent call.
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?
Explicitly says to poll after uploading until status is 'Ready' before calling get_variable_relationships, providing clear when and when-not usage. Also documents optional authorization parameter.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_variable_relationshipsGet Variable RelationshipsARead-onlyIdempotentInspect
Retrieve analyzed variable relationships for a completed paper.
Only returns results when get_analysis_status reports status='Ready'.
Without an API key (anonymous): returns the relationship list with
source, target, and direction — but detailed reasoning is available
only with an API key. Sign up at https://hypathesis.com to get one.
Args:
file_id: The file_id returned by the /upload endpoint.
authorization: Optional. API key as 'Bearer hk_...' or 'hk_...'.
Returns:
Authenticated: full details (source, target, directed, reason per
relationship; name, measure per variable).
Anonymous: gated (source, target, directed per relationship;
name per variable; sign_up_url for full access).| Name | Required | Description | Default |
|---|---|---|---|
| file_id | Yes | ||
| authorization | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds important behavioral context: results depend on authentication status, and a precondition on analysis completion. No contradictions with annotations.
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 well-structured with clear sections: purpose, precondition, authentication details, Args, Returns. Each sentence adds value, though the authentication explanation could be more compact.
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 an output schema exists, the description need not detail return values. It covers main use case, precondition, and authentication. Could mention error conditions or how to obtain `file_id`.
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 0%, so description compensates. It explains `file_id` as from the upload endpoint and `authorization` as optional Bearer key. Provides format examples for authorization.
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 what the tool does: 'Retrieve analyzed variable relationships for a completed paper.' It uses specific verb and resource, and distinguishes from siblings like `get_analysis_status` which reports status.
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 explicit precondition: returns only when `get_analysis_status` reports status='Ready'. It also explains authentication difference. However, it does not explicitly mention when not to use or alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
initiate_device_authInitiate Device AuthAInspect
Start a browser-based sign-in flow to get an API key for full access.
Call this when you need detailed analysis results (reasoning, measurements)
that require authentication. Returns a verification URL to show to the user.
After the user signs in, poll check_device_auth with the returned user_code
to get the API key.
Returns:
{
"verification_url": str,
"user_code": str,
"expires_in": 600,
"message": str
}| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are minimal (no readOnly, idempotent, destructive hints). Description adds significant behavioral context: initiation of sign-in, returns verification URL and code, expiration time, and the need for polling. No contradictions. Would benefit from mentioning auth scopes or rate limits, but still strong.
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?
Description is concise (3 sentences + return structure), front-loaded with purpose, and every sentence adds value. No wasted words.
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?
Tool has no parameters, no nested objects, and output schema is fully described. Annotations are present. Description covers purpose, usage, and next steps. Completely adequate for agent selection and invocation.
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?
No parameters in schema (100% coverage). Description adds no parameter details since none exist. Baseline of 4 is appropriate for a parameterless tool.
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?
Description states a clear verb ('start'), resource ('browser-based sign-in flow'), and outcome ('get an API key for full access'). Distinguishes from sibling tools like check_device_auth (polling) and get_analysis_status (data retrieval).
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?
Explicitly states when to call ('when you need detailed analysis results requiring authentication') and provides follow-up instructions ('poll check_device_auth'). No ambiguity.
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!