usecortex-mcp

Server Quality Checklist

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

Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes, but there is some overlap between query_knowledge and search_knowledge, as both involve searching knowledge, though their descriptions clarify different methods (AI-powered vs. keyword). The other tools target clearly different operations like adding, retrieving, listing, and capturing.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with snake_case, such as add_knowledge, capture_session, get_topic, list_sessions, list_topics, query_knowledge, recall_memory, and search_knowledge. This uniformity makes the set predictable and easy to understand.
Tool Count5/5
With 8 tools, this server is well-scoped for managing knowledge and memory, covering core operations like adding, retrieving, listing, and searching. The count is appropriate, providing comprehensive functionality without being overwhelming or insufficient.
Completeness4/5
The tool set covers key CRUD-like operations for knowledge and session memory, including add, get, list, and search. A minor gap is the lack of update or delete tools for knowledge or sessions, which might limit full lifecycle management, but agents can likely work around this.
Average 3.3/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on March 9, 2026
- 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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It states this is a write operation ('Write new knowledge'), implying mutation, but doesn't address critical aspects like permissions needed, whether writes are permanent or reversible, rate limits, or what happens on success/failure. For a mutation tool with zero annotation coverage, this leaves significant behavioral gaps.
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 concise and well-structured in two sentences. The first sentence clearly states the core purpose, and the second provides helpful examples of what can be stored. There's no wasted language, though it could be slightly more front-loaded with critical behavioral information given the lack of annotations.
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?
Given this is a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't address what happens after writing (success indicators, error conditions, return values) or important behavioral constraints. While the purpose is clear, the lack of transparency about this write operation's consequences makes it inadequate for safe agent use.
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 schema already documents both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't elaborate on 'content' format or 'topic' auto-detection logic). With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.
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 purpose: 'Write new knowledge to your base' specifies the action (write) and resource (knowledge base). It distinguishes from siblings like 'query_knowledge' or 'search_knowledge' by focusing on creation rather than retrieval. However, it doesn't explicitly contrast with 'capture_session' or 'recall_memory', leaving some sibling differentiation incomplete.
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 minimal usage guidance. It suggests storing 'facts, decisions, patterns, or any information your AI should remember', which implies context but doesn't specify when to use this tool versus alternatives like 'capture_session' for session-based storage or 'recall_memory' for memory retrieval. No explicit when-not-to-use or prerequisite information is included.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves entries, implying a read-only operation, but doesn't cover critical aspects like whether it returns all entries or is paginated, error conditions, or performance characteristics. This leaves significant gaps for a tool with no annotation support.
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, efficient sentence that directly states the tool's function without any wasted words. It is front-loaded with the core action and resource, making it easy to parse quickly.
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?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'knowledge entries' entail, the format of the returned data, or any behavioral traits like limitations or side effects. For a retrieval tool with no structured support, this leaves the agent under-informed.
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 input schema has 100% description coverage, with the 'topic' parameter fully documented in the schema. The description adds no additional semantic context beyond implying the parameter is used to filter entries by topic, which is already clear from 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Retrieve') and resource ('knowledge entries for a specific topic'), making the tool's purpose understandable. However, it doesn't distinguish this tool from sibling tools like 'query_knowledge' or 'search_knowledge', which likely have overlapping functionality, so it doesn't reach the highest score.
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 alternatives such as 'query_knowledge' or 'search_knowledge'. It mentions retrieving entries 'for a specific topic', but this is part of the purpose statement rather than explicit usage instructions, leaving the agent without clear direction on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions a requirement ('Requires Memory plan'), which adds some context about access needs. However, it lacks details on rate limits, pagination, error handling, or what 'recent' means in terms of time frame, leaving significant gaps for a tool that lists data.
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 very concise with two short sentences that directly state the purpose and a key requirement. It is front-loaded with the main action and avoids unnecessary details. However, it could be slightly more structured by explicitly separating usage notes, but overall it's efficient.
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?
Given no annotations and no output schema, the description is incomplete. It lacks information on return values (e.g., format of listed sessions), behavioral aspects like sorting or pagination, and deeper context on the 'Memory plan' requirement. For a tool with three parameters and no structured support, more descriptive detail is needed to fully guide an agent.
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 input schema has 100% description coverage, with clear documentation for all three optional parameters (tool, project, limit). The description does not add any additional meaning beyond what the schema provides, such as explaining filter logic or default behaviors. Since schema coverage is high, the baseline score of 3 is appropriate.
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 verb ('List') and resource ('recent captured session memories'), making the purpose understandable. It distinguishes from siblings like 'capture_session' (which creates) and 'recall_memory' (which retrieves specific memories). However, it doesn't explicitly differentiate from 'list_topics' or 'search_knowledge', which might also list items, so it's not fully specific to sibling context.
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 includes a prerequisite ('Requires Memory plan'), which provides some context for when to use it. However, it doesn't offer explicit guidance on when to choose this tool over alternatives like 'list_topics' or 'search_knowledge', nor does it specify exclusions or detailed usage scenarios beyond the plan requirement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions AI-based search and natural language input, which adds some behavioral context, but lacks details on permissions, rate limits, response format, or error handling. For a search tool with no annotations, this is insufficient to fully inform agent behavior.
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 with zero waste: it states the action, method, and input type efficiently. It's front-loaded with the core purpose and appropriately sized for the tool's complexity.
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 no annotations, no output schema, and moderate complexity (2 parameters, AI-based search), the description is minimally adequate. It covers the basic purpose and input type but lacks details on output, error cases, or behavioral traits. It meets the minimum viable threshold but has clear gaps in completeness.
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 schema already documents both parameters ('query' and 'topic'). The description adds marginal value by emphasizing 'natural language question' for 'query', but doesn't provide additional syntax, examples, or constraints beyond what the schema states. Baseline 3 is appropriate when schema does the heavy lifting.
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 purpose: 'Search your knowledge base using AI' with a specific verb ('search') and resource ('knowledge base'). It distinguishes from siblings like 'add_knowledge' (write vs read) and 'search_knowledge' (similar but not identical naming), though the distinction from 'search_knowledge' isn't explicit. The natural language aspect 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 alternatives like 'search_knowledge' or 'recall_memory' is provided. The description implies usage for AI-based natural language queries but doesn't specify contexts, exclusions, or prerequisites. It's a basic functional statement without comparative advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It states the tool lists topics but doesn't describe return format (e.g., list structure, pagination), performance characteristics (e.g., latency for large bases), or side effects (e.g., whether it's cached or triggers updates). This leaves significant gaps for an agent to understand how to handle results.
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, clear sentence with zero waste—it directly states the tool's purpose without fluff or repetition. It's front-loaded and efficiently communicates the core functionality, making it easy for an agent to parse quickly.
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 tool's low complexity (0 parameters, no output schema, no annotations), the description is minimally adequate but lacks completeness. It doesn't address behavioral aspects like return format or usage context, which are important even for simple tools. However, it covers the basic purpose sufficiently for a straightforward list operation.
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 has 0 parameters with 100% description coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, and the baseline for 0 parameters is 4, as it avoids unnecessary detail while matching the schema's simplicity.
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 ('List') and resource ('all knowledge topics in your base'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'get_topic' or 'search_knowledge', but the verb 'List all' implies a comprehensive retrieval rather than filtered search or single-item fetch.
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 provided on when to use this tool versus alternatives like 'get_topic' (for a specific topic), 'search_knowledge' (for filtered queries), or 'query_knowledge' (which might have different semantics). The description implies usage for retrieving all topics but doesn't specify contexts, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'Requires Memory plan,' which is a useful constraint, but doesn't describe other behavioral traits such as whether this is a read-only operation, potential rate limits, authentication needs, or what the output format looks like. For a search tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
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 concise and front-loaded, starting with the core purpose. It uses three sentences efficiently: the first states the action, the second provides usage examples, and the third adds a constraint. There's no unnecessary repetition or fluff, making it easy to parse quickly.
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 tool's complexity (a search function with 4 parameters) and the lack of annotations and output schema, the description is somewhat incomplete. It covers the basic purpose and a constraint but doesn't explain return values, error handling, or how it differs from sibling tools. This leaves the agent with gaps in understanding the full context of use.
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%, meaning the input schema already documents all parameters (query, tool, project, limit) with descriptions. The description doesn't add any additional meaning or context about these parameters beyond what's in the schema. According to the rules, with high schema coverage (>80%), the baseline score is 3 even with no param info in the description.
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 purpose: 'Search across all captured session memories using AI.' It specifies the resource (session memories) and the action (search using AI), and provides examples of what can be asked. However, it doesn't explicitly differentiate from sibling tools like 'query_knowledge' or 'search_knowledge', which likely have overlapping functions.
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 by stating 'Ask what you worked on, what decisions were made, or what patterns were found,' which suggests it's for querying past session content. It also mentions 'Requires Memory plan' as a prerequisite. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like 'query_knowledge' or 'search_knowledge', leaving the distinction unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the 'Requires Memory plan' requirement, which is useful context about access control. However, it doesn't describe other important behaviors such as whether this is a write operation (implied by 'capture' but not explicit), what happens on success/failure, rate limits, or how the data is stored/retrieved, leaving significant gaps for a tool that persists data.
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 with zero waste: the first sentence states the purpose, and the second adds crucial context about what to store and a prerequisite. It's front-loaded with the core action and appropriately sized for the tool's complexity.
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 no annotations and no output schema, the description is moderately complete: it covers the purpose and a key requirement ('Memory plan'). However, for a write tool with 5 parameters, it lacks details on behavioral outcomes (e.g., success response, error handling) and doesn't leverage the rich schema to explain usage nuances, making it adequate but with clear gaps.
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 schema already documents all 5 parameters thoroughly. The description doesn't add any parameter-specific details beyond what's in the schema (e.g., it doesn't explain format constraints for 'summary' or examples for 'tool'). With high schema coverage, the baseline is 3, and the description doesn't compensate with extra semantic value.
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 verb ('capture') and resource ('AI session summary into persistent memory'), specifying what gets stored ('what you learned, decided, or built during this session'). It distinguishes from sibling tools like 'add_knowledge' or 'recall_memory' by focusing specifically on session summaries rather than general knowledge or retrieval.
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 provides clear context for when to use this tool ('Capture an AI session summary... Store what you learned, decided, or built during this session') and mentions a prerequisite ('Requires Memory plan'). However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools, such as when to use 'add_knowledge' instead for non-session data.
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 carries full burden. It describes the search method ('keyword (text match)') but doesn't disclose behavioral traits like pagination, result limits, sorting, or error conditions. The description adds some context about the search type but lacks operational details.
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 zero waste - first states purpose and method, second provides clear alternative. Perfectly front-loaded with essential information in compact form.
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 search tool with 100% schema coverage and no output schema, the description is reasonably complete. It covers purpose, differentiation from alternatives, and search method. However, without annotations or output schema, it could benefit from more behavioral context about what results look like.
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 schema already documents the single 'query' parameter. The description adds minimal semantic context by mentioning 'keyword' and 'text match', but doesn't provide additional syntax, format, or usage details beyond what the schema 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 specific action ('Search knowledge entries') and resource ('knowledge entries') with the method ('by keyword (text match)'). It distinguishes from sibling 'query_knowledge' by specifying this is keyword-based search versus AI-powered answers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides when to use this tool ('Search knowledge entries by keyword') versus when to use an alternative ('Use query_knowledge for AI-powered answers'). This gives clear guidance on tool selection between two similar search functions.
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:

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

Score Badge

Copy to your README.md:

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

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