A
licenseA
qualityB
maintenanceMCP server to dynamically load Claude Code skills into AI agents
5
62
15
MIT
DiscoMCP
by ieranama
🪩 DiscoMCP
Your agent, meet your tools and context.
Teach any AI agent how you use the tools it connects to — safely, in one command.
Agents are great at code. They get lost inside your tools — they don't know which of a hundred actions matter, how your data connects, or what's safe to touch. So they guess, or they freeze.
DiscoMCP fixes that. Point it at any MCP server and it hands your agent a skill for how you use it — not a generic tool list, but your workflows on your data: the views, tables and records you actually work with, the sequences that answer your real questions, and how one result leads into the next. Learned by looking at your own workspace, and without ever changing a thing.
A tool catalogue tells an agent the server has 90 actions. DiscoMCP tells it the five you actually use, in the order you use them, against the data that's really there.
Why teams use it
🎯 Tailored to how you work | Not a generic capability dump — a profile of your usage: your workflows, your conventions, the parts of your workspace that matter, grounded in what's really in your data. |
🧭 Agents that know their way around | Your agent follows the sequences that answer real questions and chains one result into the next, instead of guessing across a hundred tools. |
🔒 Read-only, guaranteed | Exploring can never write. It runs a step only when it can prove that step is a read. Nothing is deleted or modified while it learns. |
⚡ One command, zero setup | A single 8 MB binary. No runtime, no toolchain — |
Related MCP server: Agent Delegation MCP
Read-only, guaranteed
This is the part that lets you actually turn an agent loose on a real system.
DiscoMCP explores behind a default-deny gate: it runs an action only when it can prove that action is a read. A safe lookup runs. Anything that could write, change, or delete — even if a tool claims it's harmless — is refused. Secrets are stripped from everything it saves.
So your agent can learn your production tools without you holding your breath.
Does it help?
Same question, same server, same model — with and without the generated skill. Read-only, against a genuinely wide, unfamiliar server. n=2 per row.
Task | Cold (no skill) | With skill | Tokens |
Targeted lookup | ~12 round-trips | ~5 | −28% |
Cross-dataset reasoning | ~10 round-trips | ~6 | −44% |
Full pipeline trace | ~10–13 round-trips | ~3 | −57% |
The harder and less familiar the task, the more it helps: the skill front-loads the map a cold agent has to rediscover by trial. Both reach correct answers — the skill reaches them in far fewer round-trips.
Small sample, directional — not a guarantee. On a trivial task or a narrow server the skill's own prompt cost can wash out what it saves; the durable win is fewer round-trips and steadier behavior on complex servers. Full method in benchmarks/METRICS.md.
Get started
1. Run it — no install needed:
npx @ieranama/discomcp --help2. Point it at a server — the whole config is a few lines:
[targets.example]
transport = "stdio"
command = "npx"
args = ["-y", "some-mcp-server"]3. Hand it to your agent and let it explore:
discomcp serve --config ./discomcp.tomlYour agent does the exploring; DiscoMCP keeps it safe and writes the skill. The result lands in .discomcp/profiles/<server>/SKILL.md — ready to drop into your agent.
Under the hood
Built in Rust: the model does the thinking, a small deterministic core enforces every safety check. Every claim in a generated skill is tagged with how it was known — declared, documented, observed, or inferred — so an agent never mistakes a guess for a fact.
License
Licensed under either of Apache License, Version 2.0 or MIT License at your option.
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in DiscoMCP by you, as defined in the Apache-2.0 license, shall be dual-licensed as above, without any additional terms or conditions.
Available Tools
7 toolsexecute_probeA
Validate and, if permitted, execute ONE tool call against the target. DEFAULT-DENY: a probe runs ONLY IF it is provably read-only — a read-verb tool name (list/get/read/search/...), a server readOnlyHint, or a query-executor whose sql/query argument is a read-only statement (SELECT/WITH/SHOW/DESCRIBE/EXPLAIN/PRAGMA). A write-verb tool name or the destructive backstop (server destructiveHint or destructive-verb name) rejects regardless of your declaration. Your classification is REQUIRED but ADVISORY — recorded as evidence, it never authorizes execution. Also enforces JSON-schema validation, anti-fabrication provenance (an argument that DECLARES an observed source must cite a value actually captured — otherwise rejected; providing provenance is optional but citing a non-existent observation is fabrication), and the probe budget. Returns a redacted observation or the rejection reason. The observation's identifiers list EVERY short leaf scalar as a candidate (name, value, json_pointer, from_tool) — your raw material to author entity names, identifiers, enums (from distinct values), and relationships. Every result includes a gaps report (unsampled_structures, unexecuted_tools, untraversed_identifiers, sampling_hints, depth_signal).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| tool | Yes | A tool name from inspect_target's tool_cards. | |
| target | Yes | ||
| arguments | Yes | Arguments for the target tool call. | |
| objective | No | Optional note on what this probe is trying to learn. | |
| provenance | No | Origin of each argument. Provide it for any identifier you took from a prior response: use kind "observed" citing the exact probe and pointer the value came from — DiscoMCP verifies that citation exists and rejects a fabricated one. Use "user_defined" ONLY for a value the human user explicitly supplied. Omitting provenance no longer rejects a probe, but never invent an identifier and claim it was observed. | |
| classification | Yes | YOUR risk classification of this tool, judged from its name, description, input_schema and annotations. ADVISORY evidence only: recorded as agent-attributed evidence in the profile, it does NOT authorize execution. DiscoMCP runs a probe only when it is provably read-only (read-verb tool name, server readOnlyHint, or a query-executor with a read-only sql/query argument); write-verb names and the destructive backstop are rejected regardless of what you declare. |
TDQS
A4.8/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavioral traits: validation, execution conditions, classification advisory, provenance checking, anti-fabrication, budget enforcement, return values (redacted observation or rejection reason), and gaps report. There is no contradiction with missing annotations.
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 lengthy but packed with essential information for a complex tool. It front-loads the purpose and then systematically explains behavioral rules. Could be slightly more concise, but the density of information justifies the length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, nested objects, no output schema), the description is remarkably complete. It explains all behaviors, return values, and edge cases (rejection reasons, gaps report). There are no obvious 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?
Schema description coverage is 83%, which is high, but the description adds significant value beyond the schema by explaining the advisory nature of classification, provenance checking rules, and the meaning of gaps report. It helps the agent understand parameter semantics more fully.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Validate and, if permitted, execute ONE tool call against the target.' It specifies it is for probe execution, distinguishing it from sibling tools like inspect_target or list_targets which serve different functions. The verb 'execute' and resource 'probe' are explicit.
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?
The description provides detailed usage guidelines: 'DEFAULT-DENY' policy, conditions for a probe to run (read-only provability), classification advisory, provenance requirements, and budget enforcement. It explicitly contrasts with destructive tools and tells when not to use. This is comprehensive guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
finalize_profileA
Synthesize the workspace model, operational model, capability profile, quality report and SKILL.md from this session's accumulated safe observations, and write the full artifact set to disk. VERIFY every claim against the captured observations first — do not assert unobserved structures, identifiers, or relationships (the anti-fabrication provenance check will reject invented observed citations), and mark authored/inferred claims distinctly from probe-observed ones. If ZERO probes were accepted, only a STUB skill is written that plainly states nothing was safely observed — no rich profile is fabricated over a bare catalogue. Pass usage_summary: YOUR narrative of how THIS user actually uses this source, reasoned from what you observed (their saved searches, folders, tracked entities, recurring queries) — not a generic capability list. This becomes the skill's 'How You Use This MCP' section and is the whole point: the skill must let an agent exploit the MCP the way this user does. Returns skill_path to report back to the user.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| target | Yes | ||
| usage_summary | No | Agent-authored: how this specific user uses this source, inferred from the observations (concrete: what they track, which tools serve their real workflow, in what order). |
TDQS
A3.9/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so description carries burden. It discloses writing to disk, verification steps, anti-fabrication checks, and stub behavior for zero probes. Lacks mention of idempotency or potential side effects but is otherwise transparent.
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 long and includes imperative warnings that are important but could be condensed. It front-loads the main purpose, then adds constraints. Some redundancy exists in the verification phrasing.
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 complexity and lack of output schema, description covers essential conditions (zero probes, verification, return of skill_path). It could detail the return format further but is sufficiently complete for an agent.
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 description coverage is 50%; only usage_summary has a description in schema. The tool description adds meaning for usage_summary but does not explain the 'target' parameter at all, leaving agents unclear about its role.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool synthesizes workspace model, operational model, capability profile, quality report, and SKILL.md from safe observations and writes them to disk. It distinguishes from siblings like generate_skill by specifying the full artifact set and finalization aspect.
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?
Description provides context for when to use: after accumulated safe observations, with verification requirements. It also covers the zero-probes edge case. However, it does not explicitly list alternative tools or when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_skillB
Regenerate SKILL.md from an existing profile directory (profile-metadata.json + tool-catalogue.json + workspace-model.json + operational-model.json).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| profile_dir | Yes |
TDQS
B3.1/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It only says 'regenerate' which implies creation/overwriting, but does not specify side effects, idempotency, required permissions, or output 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 a single, concise sentence that includes necessary details (the files involved). It is efficient with no redundancy, though could be slightly more structured (e.g., using bullet points for the file list).
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 tool with only one parameter and no output schema, the description covers the basic purpose and input. However, it lacks details on return values, error conditions, dependencies (e.g., that the directory must contain those files), and behavioral guarantees.
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?
The lone parameter 'profile_dir' is described only as a 'profile directory' in the description, with no format, path syntax, or constraints. Schema description coverage is 0%, so the description adds minimal meaning beyond the parameter name.
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 ('Regenerate SKILL.md') and the resource ('from an existing profile directory'), listing the exact files involved. It is distinct from sibling tools which focus on probing, finalization, inspection, listing, lookup, and session status.
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, nor does it mention prerequisites, when not to use it, or any context for selecting it over sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
inspect_targetB
Connect to the target MCP, list its declared tools/resources/prompts, and start a profiling session. Returns tool cards (name, description, input schema, raw server annotations, backstop_blocked advisory) as raw material — YOU classify each tool's risk; DiscoMCP never keyword-guesses.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| goal | No | Optional objective to focus exploration. | |
| target | Yes |
TDQS
B3.1/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so description carries full burden. It discloses the main actions (connect, list, start profiling) and that it returns tool cards for risk classification, but does not explain side effects (e.g., network calls, state changes), safety, or idempotency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences front-loading the main action. The second sentence adds a useful instruction about risk classification. Could be slightly more structured but is appropriately concise.
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?
Covers the main actions and output format adequately, but lacks details about the profiling session workflow, error handling, prerequisites for connecting to a target, and how it fits with sibling tools. Without an output schema, the description provides a text explanation of the return format, which is helpful.
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 has 50% description coverage (only 'goal' is described). The description does not add meaning to 'target' or elaborate on parameter usage beyond what is in the schema. 'Target' remains undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the verb 'Connect', 'list', and 'start', and the resource 'target MCP' and its declared tools/resources/prompts. Distinguishes from siblings like list_targets (which lists targets) and lookup_target (looks up a specific target) by being the tool that initiates a profiling session.
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 like execute_probe or finalize_profile. The description does not mention prerequisites, context of use, or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_targetsA
List target MCP ids configured in this DiscoMCP server.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
A3.6/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description implies a read-only operation but does not explicitly state behavioral traits like idempotency, speed, or authorization needs. Without annotations, the description carries the full burden; it is accurate but minimal.
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 a single, front-loaded sentence with no wasted words. It could be slightly more informative but remains concise.
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 simplicity (no parameters, no output schema), the description is minimally adequate. However, it does not mention return format or potential use context, leaving room for improvement.
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?
No parameters are defined, and schema coverage is 100%. The description does not need to add parameter information; baseline score of 4 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 lists target MCP IDs, using a specific verb and resource. It distinguishes from siblings like 'inspect_target' or 'lookup_target', which likely provide more detail.
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 vs. alternatives. There is no mention of use cases, prerequisites, or relationships to sibling tools like 'inspect_target'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lookup_targetA
Check whether a DiscoMCP skill already covers this target's current declared catalogue, without probing. A MISS (no existing_skill_dir) means dispatch a BACKGROUND subagent to profile the target (inspect_target -> execute_probe gap loop -> finalize_profile) and keep working the user's task while it runs.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| target | Yes |
TDQS
A4.4/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description fully discloses behavior: it is a read-only check that does not probe, and on MISS it triggers background profiling while allowing the user's task to continue. This goes beyond basic disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler, efficiently packing purpose, behavioral constraints, and conditional workflow in a clear, front-loaded manner.
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?
The description covers the main purpose and flow, but omits explicit mention of return values (e.g., what does a HIT look like?) and error handling. However, given the tool's nature and sibling context, it is mostly complete.
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 0% and the description does not elaborate on the 'target' parameter beyond implying it is the target of interest. It does not specify format, constraints, or examples, so the description adds little value over the raw schema.
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 ('Check whether') and resource ('DiscoMCP skill... covers this target') and distinguishes itself from the probing sibling tools by explicitly stating 'without probing' and referencing the alternative workflow (inspect_target, execute_probe).
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?
The description provides explicit when-to-use guidance: use this tool to check coverage to avoid unnecessary probing. It also tells what to do on a MISS (dispatch background subagent) and maintains user task continuity, clearly differentiating from full probing tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
session_statusA
Return the current GAP REPORT for an active session, computed only from state already gathered — no target calls, no probe consumed. Reports (does not decide): unsampled_structures (collections listed but never drilled into), unexecuted_tools (unprobed tools minus backstop-blocked; you judge which are read-safe, with why_useful), untraversed_identifiers (ids seen in output but never used as a get-by-id argument, with likely_consumer_tools), sampling_hints (schema params like orderBy/pageSize/q/filter on unused tools for smart sampling), and depth_signal (raw coverage counts + probe budget). The same report rides every execute_probe result under gaps. You decide when coverage is enough.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| target | Yes |
TDQS
A3.5/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explicitly states it does not make target calls or consume probes, and clarifies it reports but does not decide. It details exactly what the report contains. With no annotations, this provides good 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single moderate-length paragraph that front-loads the main purpose and efficiently lists report components. Minor clutter from parenthetical details, but overall well-structured.
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 description thoroughly explains all report components despite no output schema. However, the missing definition for the 'target' parameter leaves a significant gap for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has one required parameter 'target' with no description in schema (0% coverage). The tool description does not explain what 'target' means or how to use it, failing to add the necessary 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 it returns a 'GAP REPORT' for an active session, listing specific components (unsampled_structures, unexecuted_tools, etc.). It distinguishes from siblings like execute_probe and finalize_profile by emphasizing it does not make target calls or consume probes.
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 safe usage ('no target calls, no probe consumed') and notes the report is also returned with every execute_probe result, but does not explicitly state when to call this tool standalone versus relying on the attached report. No alternatives or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
7 tool updates
v0.1.0- First observed
execute_probe - First observed
finalize_profile - First observed
generate_skill - First observed
inspect_target - First observed
list_targets - First observed
lookup_target - First observed
session_status
TDQS
A3.8/5.0
Disambiguation5/5
Each tool has a distinct role in the profiling lifecycle: executing probes, finalizing profiles, inspecting targets, etc. No two tools overlap in purpose.
Naming Consistency4/5
Six of seven tools follow the verb_noun pattern (execute_probe, finalize_profile, generate_skill, inspect_target, list_targets, lookup_target). Only session_status deviates with a noun_noun format.
Tool Count5/5
Seven tools cover the entire profiling workflow from target discovery to skill generation without being excessive. Each tool serves a necessary step.
Completeness4/5
The tool set covers the core profiling operations but lacks profile management features like deletion or session cancellation, which are minor gaps.
Maintenance
ActivitySlowing
ResponsivenessNo issues
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
MCP server teaching AI agents to implement TideCloak: auth, E2EE, IGA, security analysis
Hosted MCP server connecting claude.ai, ChatGPT and other AI apps to your own computer
Official MCP server for Agentwork — delegate tasks to AI agents with human-in-the-loop
Cloud-hosted MCP server for durable AI memory
Related MCP Servers
- AlicenseNot gradedqualityBmaintenanceAgent Delegation - MCP server providing AI-powered tools and automation by MEOK AI Labs15MIT
- AlicenseNot gradedqualityFmaintenanceConsciousness Engine - MCP server providing AI-powered tools and automation by MEOK AI Labs15MIT
- AlicenseAqualityCmaintenanceEnables AI assistants to discover, install, configure, and manage MCP servers through natural language conversation, automating tedious manual setup across multiple clients.11MIT
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
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/ieranama/discomcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server