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kothar

by yahiaklk
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kothar

kothar MCP server

Context-aware MCP server advisor. Tells you what to install for your specific project — and why.

The problem

Glama has 19,000+ MCP servers. You have a project. Nobody bridges the gap.

LLMs asked directly hallucinate servers that don't exist and recommend from stale training data. Directories give you search, not advice.

kothar fills the selection under context gap: not "here are 19,000 options" but "for your specific project, right now, here's what you need and why."

The two moments nobody is serving

Project start: "I'm building a Python data pipeline with DuckDB and FastAPI" → what do I install right now

Mid-project: "I just added an auth layer / I need to handle PDF ingestion" → what do I add now that I've reached this point

The second moment is more valuable. At project start, people can Google. Mid-project they're in flow.

Three tools

recommend_for_project(description)
  → top MCP servers for your stack with rationale

recommend_for_next_step(current_stack, new_context)
  → what to add as your project evolves

explain_fit(server_name, project_description)
  → why a specific server fits your project

Install

Prerequisites: uv

git clone https://github.com/yahiaklk/kothar
cd kothar
uv sync

Build the index (first run, ~30s):

uv run python -m kothar.indexer

Add to Claude Code

claude mcp add --scope user kothar -- uv run --directory /path/to/kothar python -m kothar.server

Add to Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "kothar": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/kothar", "python", "-m", "kothar.server"]
    }
  }
}

Usage

Once connected, ask your AI assistant:

recommend_for_project("Python FastAPI backend with PostgreSQL and JWT auth")

recommend_for_next_step("github,filesystem", "adding Stripe payments and PDF invoices")

explain_fit("postgres", "multi-tenant SaaS with row-level security")

How it works

  • Parses awesome-mcp-servers (2000+ curated servers)

  • Embeds descriptions with all-MiniLM-L6-v2 (local, no API cost)

  • Stores in DuckDB, queries with cosine similarity

  • Template-based rationale — grounded in the registry, not hallucinated

Rebuild the index

uv run python -m kothar.indexer --force

Docker

Multi-stage image with the embedding model + DuckDB index baked in — no runtime network dependency.

docker build -t kothar:0.3.0 .
docker run --rm -i kothar:0.3.0   # stdio transport, for local MCP clients

Wire into Claude Desktop:

{
  "mcpServers": {
    "kothar": {
      "command": "docker",
      "args": ["run", "--rm", "-i", "kothar:0.3.0"]
    }
  }
}

Non-root user (uid=10001), pinned Python 3.12, deps resolved from uv.lock, model cached under /app/.hf_cache with HF_HUB_OFFLINE=1 at runtime.

Stack

Python · FastMCP · DuckDB · sentence-transformers · uv

License

MIT

Available Tools

4 tools
explain_whyB

Explain why a specific MCP server is a good fit for a given project. Example: server_name="github", project_description="open source Python library with CI/CD"

ParametersJSON Schema
NameRequiredDescriptionDefault
server_nameYes
project_descriptionYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

B3.3/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations and description only states purpose. Does not disclose behavior like error handling, required permissions, or output format beyond what output schema might provide.

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?

Description is a single sentence plus example, which is concise. However, the structure could be improved by adding a brief usage context.

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 low complexity and presence of output schema, description is minimally adequate but lacks usage guidelines and behavioral details that would make it fully self-contained.

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%, meaning parameters have no descriptions. The description provides an example usage but does not explain parameter semantics beyond the example.

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?

Describes a clear verb+resource combination: 'Explain why a specific MCP server is a good fit for a given project.' This distinguishes it from sibling recommendation 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?

Provides an example but no explicit guidance on when to use this tool versus the sibling recommendation tools. The example helps but does not fully clarify context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

recommend_for_goalA

Decompose a multi-part goal into sub-queries and recommend MCP servers for each part. Splits on hard boundaries: '. ', '; ', ' then ', ', then ', ' and then ' (not bare ' and '). project: optional project context prepended to each sub-query for richer semantic matching. Example: goal="integrate GitHub. add Stripe payments", project="Python FastAPI backend"

ParametersJSON Schema
NameRequiredDescriptionDefault
goalYes
projectNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries the burden. It explains splitting behavior and project prepending but does not disclose whether the tool is read-only or destructive, side effects, or error handling. Adequate but lacks safety profile.

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?

Three sentences plus example, no wasted words. Front-loaded main purpose, then splitting details, then optional parameter. Could be more structured but efficient.

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?

Output schema exists, so return values not required. Explains input and behavior well, but lacks usage guidelines for sibling differentiation and does not mention prerequisites or error conditions. Fairly complete for a tool with output schema.

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 0%, yet the description adds meaning: 'goal' is a multi-part goal with splitting rules, 'project' is optional context prepended. This goes beyond the schema's minimal type info.

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?

Clearly states the tool decomposes multi-part goals into sub-queries and recommends MCP servers. Distinguishes from siblings like recommend_next and recommend_for_project by focusing on multi-part goals and splitting logic.

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?

Provides splitting rules and optional parameter description but does not explicitly state when to use this tool versus alternatives like recommend_for_project or recommend_next. Usage is implied for multi-part goals.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

recommend_for_projectA

Given a project description, recommend the top MCP servers to install and explain why each one fits. Example: "Python FastAPI backend with PostgreSQL and JWT auth"

ParametersJSON Schema
NameRequiredDescriptionDefault
descriptionYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.2/5.0
Behavior3/5

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 implies a read-like recommendation behavior but does not disclose details like whether it modifies state, requires authentication, or has limitations on input length.

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 extremely concise with two sentences and an example, front-loading the core purpose. Every sentence adds value.

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 existence of an output schema, the description does not need to detail return values. It covers what the tool does and gives an example, but could mention that it returns a list of servers with explanations. The hint 'top' could be clarified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% coverage, so the description must add meaning. It specifies that the parameter is a 'project description' and provides an example, which clarifies the expected format beyond the bare 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 clearly states the tool's function: given a project description, it recommends MCP servers and explains why. It distinguishes from siblings like 'recommend_for_goal' by specifying the input is a project description, not a goal.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use (when you have a project description) with an example, but does not mention when not to use or explicitly contrast with sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

recommend_nextA

Mid-project advisor: given your current MCP stack (list of server names) and a new development context, recommend what to add next and why. Example: current_stack=["github", "filesystem"], new_context="adding Stripe payments and PDF invoices" session_file: optional path to a session notes file whose content is appended to new_context.

ParametersJSON Schema
NameRequiredDescriptionDefault
current_stackYes
new_contextYes
session_fileNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full transparency burden. It explains the tool recommends with reasoning and mentions an optional session_file. The output schema exists but is not shown; the description does not cover output format, but the example hints at a recommendation with reasons. Overall, it is sufficiently transparent for a recommendation 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 two sentences plus an example, front-loaded with purpose, and contains no unnecessary words. It is efficiently structured and easy to parse.

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 recommendation nature and the presence of an output schema (which handles return value documentation), the description covers all essential input semantics and usage context with an example. It is fully complete for agent decision-making and invocation.

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 0%, so the description must compensate. It explains current_stack as 'list of server names', new_context as 'development context', and session_file as 'optional path to session notes file whose content is appended to new_context'. This adds meaningful context beyond the schema. A slight improvement could clarify the format of current_stack entries.

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 recommends what to add next given current stack and new context, with an example. It distinguishes from sibling tools (explain_why, recommend_for_goal, recommend_for_project) by focusing on 'next additions' in a mid-project advisor role.

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 as a 'mid-project advisor' and provides an example, giving clear context. However, it does not explicitly state when not to use this tool or contrast with alternatives, slightly limiting guidance.

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.

  1. 2 tool updatesv0.3.0
    • Addedrecommend_for_goal
    • Changedrecommend_next3 fields changed
      • addedInput schema / properties / current_stack / items
        Added value: +{
+  "type": "string"
+}
      • changedInput schema / properties / current_stack / type
        Previous value: -"string"New value: +"array"
      • addedInput schema / properties / session_file
        Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null
+}
  2. 3 tool updatesv0.1.2
    • First observedexplain_why
    • First observedrecommend_for_project
    • First observedrecommend_next
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TDQS

A3.9/5.0
Disambiguation5/5

Each tool has a distinct purpose: explaining a specific server, recommending for a multi-part goal, for a full project, or for next steps. No overlap.

Naming Consistency4/5

All names follow verb_noun pattern (explain_why, recommend_for_goal, etc.), but 'explain_why' uses 'why' as a noun, slightly deviating from standard action-object convention.

Tool Count5/5

Four tools cover the essential recommendation scenarios without redundancy. The count is well-scoped for the server's purpose.

Completeness4/5

The set covers main use cases: explaining, recommending for goals, projects, and next steps. Missing features like comparing or updating recommendations, but not critical.

Maintenance

ActivityNo data
ResponsivenessSyncing

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