DevMemory MCP

Server Quality Checklist

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

Latest release: v1.0.0
Disambiguation3/5
Tools like search_all_projects (timeline mode) and get_commit_timeline both retrieve commit timelines, and summarize_commit and explain_code_changes both analyze a commit's diff, which could cause confusion. However, each tool has a distinct primary purpose (searching vs fetching, summarizing vs explaining), and descriptions provide context.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with snake_case: list_projects, search_all_projects, get_commit_timeline, etc. This makes it easy to predict behavior from the name.
Tool Count5/5
9 tools is well within the typical range for a domain-specific server. Each tool addresses a distinct aspect of Git analysis and reporting, so none feel redundant.
Completeness4/5
The tool surface covers the full workflow: project discovery, searching, timeline retrieval, commit analysis, work session grouping, TODO detection, report generation, and context restoration. Minor omissions like a direct diff fetcher or project management actions are acceptable given the server's read-only analysis focus.
Average 3.6/5 across 9 of 9 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 7 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
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, open-world, idempotent, and non-destructive. The description adds minimal behavioral context beyond that, only mentioning 'via DevMemory' without explaining what that entails (e.g., external service calls, data access, or limitations).
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 is front-loaded with the purpose and contains no redundant wording. The parenthetical 'DevMemory(데브메모리)' is slightly unnecessary but does not detract from conciseness.
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 7 parameters, no required fields, and no output schema, the description is insufficiently contextual. It doesn't explain how parameters should be combined (e.g., repoUrl vs repoPath, date vs since), nor what the output representation looks like. The schema partially covers parameter meaning but not overall usage 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?
The input schema covers 100% of parameters with descriptions, so the baseline is 3. The description itself adds no parameter semantics—it doesn't clarify which parameters to use together or provide any details beyond the schema.
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 with a specific verb ('explains') and concrete details (which file, function, and lines changed, plus their purpose). However, it does not explicitly distinguish this tool from sibling tools like 'summarize_commit', which may overlap in functionality.
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. The description only outlines what the tool does, not when it should be invoked, leaving the agent to infer usage from the schema and sibling context.
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?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is a safe read operation. The description adds useful context about the DevMemory (데브메모리) source and the daily/weekly format, but does not disclose output details or limitations.
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 front-loads the verb and resource. It avoids fluff and provides essential information without unnecessary detail.
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 no output schema, the description should explain what the report looks like or how it is returned. It also lacks guidance on parameter selection for different environments (repoUrl vs repoPath), which is a significant gap given the tool has 5 parameters.
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 80% (4 of 5 params have descriptions), so the schema already provides most parameter meaning. The description adds 'daily/weekly' which aligns with the format enum, but does not explain the distinction between repoUrl and repoPath or provide example 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 a specific verb ('Generates') and resource ('dev report'), and specifies the data source ('commits and diffs'). This distinguishes it from sibling tools like get_commit_timeline or summarize_commit, which focus on individual commits or timelines.
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 this tool versus alternatives like get_commit_timeline or summarize_commit. The description does not mention prerequisites (e.g., having a git repo) or scenarios where a simpler tool might be more appropriate.
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?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the safety profile is clear. The description adds context about the mechanism ('via DevMemory') and the action of suggesting next steps, but does not disclose how 'last work' is determined or any limitations. No contradiction 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that conveys the core functionality without unnecessary words. It is efficiently written and easy to parse.
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 read-only tool with two optional parameters and no output schema, the description is adequate. It states the main purpose and outcome (restores context, suggests next steps), but could be more specific about return format or required inputs. Given the annotations and schema, it is reasonably complete.
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 descriptions for both repoUrl and repoPath, so the baseline is 3. The description does not add any parameter-specific meaning beyond what is already in the schema; it only mentions the overall operation.
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?
Purpose is clear: 'Restores last work context and suggests next steps' gives a specific verb (restore/suggest) and resource (last work context). It distinguishes from siblings like list_projects or get_commit_timeline, though it could more explicitly contrast with get_work_session or find_unfinished_tasks which are related to work state.
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 want to resume previous work, but gives no explicit guidance on when to use this versus sibling tools, nor exclusions. There is no mention of prerequisites like needing an existing memory entry or how repoUrl/repoPath should be chosen.
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?
Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds mode names, but these are also present in the schema enum. It does not disclose return format, rate limits, or other behavioral traits beyond what annotations already imply.
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 concise: two short sentences, front-loaded with the core action. The mode enumeration is slightly redundant but not wasteful, maintaining high efficiency.
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 9 parameters and no output schema, this description is too sparse. It does not explain what the tool returns or how different modes affect output. The schema handles parameter details, but high-level behavior and result context are missing.
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 all 9 parameters documented in detail. The description adds no extra parameter semantics beyond what the schema provides, 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 clearly states the action ('Searches') and resource ('multiple Git projects at once'), differentiating from siblings that likely focus on single projects. The mode list adds specificity without ambiguity.
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 phrase 'at once' implies use for cross-project searches, but there is no explicit guidance on when to prefer this over siblings like get_commit_timeline or find_unfinished_tasks. No exclusion criteria or alternative comparisons are provided.
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?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds 'via DevMemory' indicating external service dependency, but no further behavioral context like rate limits or failure modes. No contradiction 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with the core action and output. No wasted words; every element contributes to understanding.
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 the low complexity (3 params, 1 required) and strong annotations (read-only, idempotent), the description is nearly sufficient. It clarifies the return type (feature classification) but does not explain what the classification contains or how to handle repoUrl vs repoPath selection. Still, for a simple tool with full schema coverage, this is adequate.
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 all parameters described (repoUrl, repoPath, commitHash). The tool description adds no parameter-specific meaning beyond the schema, 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 uses a specific verb ('Analyzes') and resource ('commit diff'), and states the output ('feature classification via DevMemory'). It clearly distinguishes from siblings like get_commit_timeline or explain_code_changes by focusing on classification.
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. Sibling tools like explain_code_changes could plausibly overlap, but the description does not address selection criteria or 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?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds that detection happens 'via DevMemory', but doesn't disclose other behavioral aspects like scope or return behavior. No contradiction 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, compact sentence that is front-loaded with the main action and object. Every word earns its place, with no redundancy or filler.
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?
The tool is simple and schema/annotations are rich, but there is no output schema and the description doesn't explain what the tool returns or what 'unfinished tasks' looks like in practice. This leaves a minor gap in understanding the tool's output.
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 each parameter documented. The description adds no additional parameter semantics beyond what the schema already provides, so 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Detects') and specifies the resource (TODO, FIXME, unimplemented markers), clearly distinguishing it from sibling tools that focus on projects, commits, and reports. The name and description align well.
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—detecting unfinished tasks—but provides no explicit when/when-not guidance or mention of alternatives. Since no sibling tool directly overlaps, the implied context is somewhat sufficient, but explicit guidance is lacking.
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?
Annotations already mark it read-only and idempotent; the description adds that it operates via DevMemory and is scoped to a single date. It does not disclose return shape or edge cases, but it does not contradict annotations.
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 one focused sentence that front-loads the action and scope. No filler or repetition.
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?
While the schema and annotations cover safety and parameters, the description lacks output-shape details and guidance on repo selection. For a read-only aggregation tool with no output schema, this is adequate but leaves 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 descriptions cover all three parameters, including date format and the purpose of repoUrl versus repoPath. The tool description adds no further parameter detail, so a baseline of 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 a specific action: grouping commits on a given date into work sessions. It distinguishes this tool from siblings like get_commit_timeline or summarize_commit by focusing on session-level aggregation.
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 daily work-session aggregation is needed but provides no explicit when-to-use or alternative guidance. It does not mention cases where get_commit_timeline or other siblings would be more appropriate.
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?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds value by revealing the data source (devmemory.projects.json or DEVMEMORY_PROJECTS env), which is useful for understanding configuration dependency. No contradiction 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with the core action and resource. The Korean parenthetical is brief and adds product context without waste. Every word earns its place.
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 list tool with no params, the description covers the primary purpose and data source. It does not detail the return shape, but the phrase 'returns registered Git projects' implies a list of project objects. Given no output schema, this is adequate, though a brief note on return format would be slightly better.
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 baseline is 4. Description correctly avoids parameter details. Schema coverage is vacuous at 100%, so no compensation needed.
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 action ('Returns') and resource ('registered Git projects for DevMemory'). It mentions the data source, which adds specificity. However, it does not explicitly differentiate from the sibling 'search_all_projects', though the name 'list_projects' implies listing vs searching.
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 guidance on when to use this tool versus alternatives. The description says what it reads, but does not state conditions like 'use to get all registered projects' or 'prefer search_all_projects when filtering'. The context is only implied.
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?
Annotations already cover read-only and idempotent behavior, so the description does not need to restate those. It adds meaningful context by mentioning 'local repo or public GitHub URL via DevMemory', clarifying the two distinct data sources and the integration mechanism. This goes beyond the structured annotations and helps set expectations about tool scope.
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, focused sentence that front-loads the verb and resource, then adds key context in minimal words. There is no redundancy, and every piece of information earns its place. It is easy for an agent to parse and act on.
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 read-only fetch tool with no required parameters and a fully documented schema, the description provides adequate situational context (local vs remote sources) and does not need to detail return formats, as the output is self-explanatory as a commit timeline. The lack of output schema is compensated by the simplicity of the tool's purpose. The sibling list offers additional context, but the description is sufficient on its own.
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 all 7 parameters have detailed descriptions including examples and usage notes. The main description adds no additional parameter-level semantics; it merely refers to 'local repo' and 'public GitHub URL', which is already implied by repoPath and repoUrl parameters. The schema does the heavy lifting, so the baseline of 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 fetches 'commit timeline', specifying both local and public GitHub URL sources. This differentiates it from siblings like summarize_commit or explain_code_changes, which analyze rather than retrieve. The action verb 'Fetches' and resource 'commit timeline' are precise and unambiguous.
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 (for fetching commit history from local or public GitHub) but does not explicitly contrast it with alternatives. Sibling tools exist for different tasks (e.g., summarize_commit for summarization), yet no direct 'use this instead of...' guidance is given. The schema descriptions provide some contextual hints, but the main description lacks explicit exclusions.
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:

[![devmemory-mcp MCP server](https://glama.ai/mcp/servers/KIMHYUNWOO313/devmemory-mcp/badges/card.svg)](https://glama.ai/mcp/servers/KIMHYUNWOO313/devmemory-mcp)

Score Badge

Copy to your README.md:

[![devmemory-mcp MCP server](https://glama.ai/mcp/servers/KIMHYUNWOO313/devmemory-mcp/badges/score.svg)](https://glama.ai/mcp/servers/KIMHYUNWOO313/devmemory-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/KIMHYUNWOO313/devmemory-mcp'

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