tero-mcp-lite

Server Quality Checklist

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

Latest release: v0.2.0
Disambiguation5/5
Each tool serves a distinct purpose: querying by status, kind, or ID; full-text search; graph traversal; citation retrieval; query analysis; index refresh; and server identity. No two tools overlap in function, making selection unambiguous.
Naming Consistency3/5
Naming follows a mix of patterns: three tools use 'query_by_*', while others use bare verbs (cite, explain, refresh, identify) or compound nouns (cross_ref, text_search). The snake_case convention is consistent, but the verb/noun order varies.
Tool Count5/5
With 9 tools, the server is well-scoped for a query-focused corpus service. Each tool earns its place, covering search, lookup, analysis, and maintenance without unnecessary bulk.
Completeness4/5
Core corpus operations are covered: retrieval by ID, status, kind, text search, and dependency traversal. Minor gaps exist, such as a dedicated tool to list all statuses or kinds, but the existing tools allow agents to work around this.
Average 3.1/5 across 9 of 9 tools scored. Lowest: 1.8/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 38 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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.
Add a glama.json file to provide metadata about your server.
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

Behavior1/5
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 does not mention side effects, return format, authentication requirements, or whether the operation is read-only. The cryptic one-liner gives the agent no insight into the tool's runtime behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise as a single sentence, but it is under-specified and poorly structured. While brevity is positive, the sentence is cryptic and does not clearly convey the tool's purpose, making the conciseness counterproductive.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 parameters, 2 required, and no output schema or annotations, this description is grossly inadequate. It does not explain return values, expected input combinations, or the meaning of 'citations' in this context, leaving the agent with insufficient information to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is only 40% (kind and token have descriptions), and the description adds little. It says 'kind + its args' but does not explain depth, start, or value parameters, nor how they relate to specific kind values. The description fails to compensate for the undocumented parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Citations only for a query (kind + its args, as query_*)' is vague and lacks a clear verb-resource structure. It doesn't specify what 'citations' means or what the tool actually does with the query, making it hard to distinguish from sibling tools.
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?
There is no explicit guidance on when to use 'cite' versus the sibling query tools (e.g., query_by_status, query_by_kind). The reference to 'query_*' implies a connection but does not explain the appropriate context or alternatives, leaving the agent without clear selection criteria.
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 supplied, so the description carries the full burden. It only hints at non-execution with 'trace only', but does not explicitly state side effects, permissions, output format, or error behavior. This is insufficient for a tool with 5 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words, which is structurally concise. However, it is too terse and uses jargon ('EXPLAIN trace', 'query_*') that obscures clarity, so it could be more informative while remaining concise.
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?
With 5 parameters, no output schema, and no annotations, the description is incomplete. It does not mention return values, parameter details beyond a vague 'kind + args', prerequisites, or any contextual information. The link to query_* tools is the only context provided, which is insufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 20% (only 'token' has a description). The description adds a hint that 'kind + its args' correspond to query_* tools, which gives some meaning to 'kind' and the other parameters, but it leaves depth, start, and value unexplained, providing minimal compensation for the low coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies an operation ('EXPLAIN trace') and a scope ('for a query (kind + its args, as query_*)'), which differentiates it from sibling query tools. However, the meaning of 'EXPLAIN trace' is ambiguous and relies on domain knowledge, making it only vaguely clear.
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 explicit when-to-use guidance is provided. The reference to 'query_*' implies a connection to sibling tools, but it does not state when to choose this tool over query_by_kind, query_by_status, or others, nor does it mention any exclusions or alternatives.
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?
With no annotations, the description must carry the behavioral burden, but it only says 'All rows' which implies a read operation without filtering. It does not disclose authentication requirements, pagination, error handling, or whether the result is limited to a specific set of fields.
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 concise sentence that gets straight to the point. No filler or redundant information.
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 no output schema and minimal annotations. The description does not explain what the response contains, how to handle invalid kinds, or when to prefer this over sibling tools. For a 2-parameter tool this is sparse, but not wholly inadequate.
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 50%: 'token' is described, but 'value' is not. The description adds meaning by indicating 'value' is a kind and gives examples, but it uses an ellipsis and does not enumerate valid values or specify format, leaving ambiguity.
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 lists all rows of a given kind, with examples of valid kinds (rfc, adr, note, issue, section). This distinguishes it from sibling tools like query_by_status or query_by_id, which operate on different criteria.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. No mention of prerequisites, exclusions, or how it compares to query_by_status, query_by_id, or text_search.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does not mention whether matches are exact, case-sensitive, paginated, or how results are ordered. The description is too sparse to provide meaningful transparency.
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?
One sentence with relevant examples, no filler. Although it is a sentence fragment, it communicates the essential functionality efficiently.
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?
For a simple query tool, the description gives a minimal overview but omits details such as output format or any limitations. With no output schema and no annotations, more context (e.g., whether all fields are returned or if there is pagination) would be valuable. Still, the tool's simplicity keeps this from being severely inadequate.
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 describes 'token' but not 'value'. The description partially compensates by indicating 'value' contains a status (e.g., Accepted, todo, done), giving the parameter context. However, it doesn't specify constraints like case sensitivity or allowed formats, and the 50% schema coverage leaves room for more.
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 identifies the tool as returning rows filtered by a status field, with examples of status values. It distinguishes itself from sibling tools like query_by_id and query_by_kind, though it lacks an explicit verb like 'retrieves'.
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 intended usage is implied by the name and description ('filter by status'), but there is no explicit guidance on when to choose this over alternatives like text_search or cross_ref. No exclusionary conditions are mentioned.
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?
With no annotations, the description carries full responsibility for disclosing behavior. It only lists output items and does not mention side effects, safety, permissions, or any operational context. For a tool with a bearer token parameter, it does not state whether the operation is read-only or if any state changes occur.
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, compact sentence that lists the three key outputs in a clear order. It contains no filler or redundant phrasing, making it appropriately sized and front-loaded.
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 one-parameter tool with no output schema, the description sufficiently covers return content (identity, version, gate status). It lacks broader context such as error conditions or prerequisites beyond the token, but the scope is simple enough that this is a minor gap.
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% for the single parameter 'token', which is described as 'bearer token (from TERO_TOKENS)'. The description adds no parameter-specific meaning, so the baseline score of 3 applies since the schema already fully documents the parameter.
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 provides server identity, version, and Layer-2 gate status. It names specific resources, though it lacks an explicit verb like 'get' or 'retrieve'. It does not explicitly distinguish from sibling tools, but its focus on server metadata sets it apart from query/search tools.
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: when you need server identity, version, or gate status, this is the tool. However, it provides no explicit guidance on when not to use it or comparisons to alternatives like query_by_status or explain. There is no direct mention of alternatives 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?
With no annotations, the description carries the full burden. It only states ranking and search scope but does not disclose authentication requirements, return format, pagination, or side effects. 'Ranked' adds minimal behavioral context.
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, well-structured sentence that is front-loaded and free of irrelevant detail, making it highly concise.
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?
Without an output schema or annotations, the description should explain return values and usage context. It only states scope and ranking, leaving gaps about response structure, error handling, and token usage. This is incomplete for a search 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 coverage is 100%, so the baseline is 3. The description adds no additional parameter semantics beyond the schema's own descriptions for token and 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 tool performs a ranked free-text search over id/title/summary, using a specific verb and resource scope. It distinguishes itself from sibling query_by_* tools which likely use structured queries.
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 free-text search over specific fields but does not explicitly state when to prefer this tool over alternatives like query_by_id or query_by_status, nor any 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 BFS traversal but does not state whether the operation is read-only, what the output format looks like, if there are any limits on depth or result size, or whether authentication via token is required. This is a significant gap for a traversal 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?
The description is a single, well-structured sentence that front-loads the core action and key parameters. It is concise and free of redundant information, earning every word.
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?
For a graph traversal tool with no output schema and no annotations, the description is incomplete. It does not explain what the result contains, how depth affects the traversal, or how the token is used. It also lacks any guidance on when to choose this tool over siblings, making it insufficient for an agent to confidently invoke it in a complex workflow.
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 description adds meaning to the 'start' parameter by describing it as an 'id/anchor', which is missing from the schema. It also implicitly clarifies 'depth' as a hop count, though the schema already provides that. The token parameter is well-described in the schema. Overall, the description compensates for the missing start description without fully explaining all parameters.
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 function: a breadth-first walk over depends_on/doc_refs edges from a start id/anchor. This specific verb+resource+method distinguishes it from sibling tools like query_by_id (direct lookup) and text_search (text-based search). It is unambiguous and actionable.
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 when to use the tool—when you need to explore transitive dependencies or references from a starting node. However, it does not explicitly state when not to use it or compare to alternatives such as query_by_status or query_by_kind. The usage context is clear but lacks 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. It only discloses that the lookup is exact, but does not describe the return format, error behavior when no match is found, or any side effects. While it is reasonable to infer a read-only operation, that is not explicitly stated.
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, front-loaded with the key fact ('Exact lookup by corpus id'), and parenthetical examples add clarity without bloat. Every word contributes value; there is no waste.
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?
For a simple lookup tool with high schema coverage and no output schema, the description conveys the core purpose but omits what the tool returns (e.g., the full record) and the not-found behavior. It is minimally viable but leaves these gaps.
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 schema covers both parameters (token and value) with basic descriptions. The tool description adds meaning by clarifying that 'value' is a corpus id and provides concrete examples of valid formats, which goes beyond the schema's generic 'the id to match'.
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 performs an exact lookup by corpus id, with specific examples of id formats (RFC-0034, M-1015, DN-87). This distinguishes it from sibling tools like query_by_status or query_by_kind, which lookup by other attributes, and from text_search, which implies fuzzy search.
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 wording 'exact lookup by corpus id' strongly implies this tool is for when you have a known exact id to match. However, it does not explicitly state when not to use it or mention alternatives (e.g., use text_search for partial matches). The usage is implied rather than explicit.
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 for behavioral disclosure. It mentions the required 'refresh' scope, which is useful, but it does not disclose the side effects of reloading the index, such as whether it replaces the current index, impacts in-flight queries, or what happens if the disk read fails. For a mutation-like tool, this is a significant 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 a single, front-loaded sentence that conveys both the action and the prerequisite. Every word earns its place, with no redundancy or unnecessary detail.
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?
For a simple one-parameter tool, the essential action and scope requirement are covered. However, with no output schema and no behavioral context about the reload's impact, an agent may not know what to expect after invocation or under what circumstances to call it beyond the scope hint. It is adequate but not rich.
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 only parameter 'token' is fully described as a bearer token from TERO_TOKENS. The description adds no additional parameter semantics, so the baseline score of 3 applies per the calibration guidelines.
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 the specific verb 'reload' with the resource 'served index' and source 'from disk', clearly stating what the tool does. This distinguishes it from the sibling query tools (query_by_status, text_search, etc.), which are all read/search operations.
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 when the index on disk needs to be reloaded into the served environment, and the scope requirement ('requires the refresh scope') provides a clear prerequisite. It does not explicitly list alternatives or when-not-to-use, but the sibling tools are all query operations, making the refresh action contextually distinct and easy to select when appropriate.
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:

[![tero-mcp MCP server](https://glama.ai/mcp/servers/Aphelion-Development/tero-mcp/badges/card.svg)](https://glama.ai/mcp/servers/Aphelion-Development/tero-mcp)

Score Badge

Copy to your README.md:

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

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