kothar
kothar
Kontextbewusster MCP-Server-Berater. Sagt dir, was du für dein spezifisches Projekt installieren solltest — und warum.
Das Problem
Glama hat über 19.000 MCP-Server. Du hast ein Projekt. Niemand schließt die Lücke dazwischen.
LLMs, die direkt gefragt werden, halluzinieren Server, die nicht existieren, und empfehlen basierend auf veralteten Trainingsdaten. Verzeichnisse bieten dir eine Suche, aber keine Beratung.
kothar füllt die Lücke der Auswahl unter Kontext: nicht „hier sind 19.000 Optionen“, sondern „für dein spezifisches Projekt, genau jetzt, hier ist, was du brauchst und warum.“
Die zwei Momente, die niemand bedient
Projektstart: „Ich baue eine Python-Datenpipeline mit DuckDB und FastAPI“ → was muss ich jetzt installieren
Während des Projekts: „Ich habe gerade eine Auth-Schicht hinzugefügt / ich muss PDF-Ingestion handhaben“ → was muss ich jetzt hinzufügen, wo ich an diesem Punkt angekommen bin
Der zweite Moment ist wertvoller. Beim Projektstart können Leute googeln. Während des Projekts sind sie im Flow.
Drei Tools
recommend_for_project(description)
→ top MCP servers for your stack with rationale
recommend_next(current_stack, new_context)
→ what to add as your project evolves
explain_why(server_name, project_description)
→ why a specific server fits your projectInstallation
Voraussetzungen: uv
git clone https://github.com/yahiaklk/kothar
cd kothar
uv syncErstelle den Index (erster Durchlauf, ca. 30s):
uv run python -m kothar.indexerZu Claude Code hinzufügen
claude mcp add --scope user kothar -- uv run --directory /path/to/kothar python -m kothar.serverZu Claude Desktop hinzufügen
Füge dies zu deiner claude_desktop_config.json hinzu:
{
"mcpServers": {
"kothar": {
"command": "uv",
"args": ["run", "--directory", "/path/to/kothar", "python", "-m", "kothar.server"]
}
}
}Verwendung
Sobald die Verbindung hergestellt ist, frage deinen KI-Assistenten:
recommend_for_project("Python FastAPI backend with PostgreSQL and JWT auth")
recommend_next("github,filesystem", "adding Stripe payments and PDF invoices")
explain_why("postgres", "multi-tenant SaaS with row-level security")Funktionsweise
Analysiert awesome-mcp-servers (über 2000 kuratierte Server)
Bettet Beschreibungen mit
all-MiniLM-L6-v2ein (lokal, keine API-Kosten)Speichert in DuckDB, Abfragen mittels Kosinus-Ähnlichkeit
Vorlagenbasierte Begründung — fundiert im Verzeichnis, nicht halluziniert
Index neu erstellen
uv run python -m kothar.indexer --forceDocker
Multi-Stage-Image mit eingebettetem Embedding-Modell + DuckDB-Index — keine Laufzeit-Netzwerkabhängigkeit.
docker build -t kothar:0.3.0 .
docker run --rm -i kothar:0.3.0 # stdio transport, for local MCP clientsIn Claude Desktop einbinden:
{
"mcpServers": {
"kothar": {
"command": "docker",
"args": ["run", "--rm", "-i", "kothar:0.3.0"]
}
}
}Nicht-Root-Benutzer (uid=10001), festes Python 3.12, Abhängigkeiten aufgelöst aus uv.lock, Modell zwischengespeichert unter /app/.hf_cache mit HF_HUB_OFFLINE=1 zur Laufzeit.
Stack
Python · FastMCP · DuckDB · sentence-transformers · uv
Lizenz
Available Tools
4 toolsexplain_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"
| Name | Required | Description | Default |
|---|---|---|---|
| server_name | Yes | ||
| project_description | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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.
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.
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.
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.
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.
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"
| Name | Required | Description | Default |
|---|---|---|---|
| goal | Yes | ||
| project | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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.
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.
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.
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.
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.
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"
| Name | Required | Description | Default |
|---|---|---|---|
| description | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| current_stack | Yes | ||
| new_context | Yes | ||
| session_file | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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.
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.
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.
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.
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.
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.
2 tool updates
v0.3.0- Added
recommend_for_goal - Changed
recommend_next3 fields changed- added
Input schema / properties / current_stack / itemsAdded value: +{ + "type": "string" +} - changed
Input schema / properties / current_stack / typePrevious value: -"string"New value: +"array" - added
Input schema / properties / session_fileAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null +}
3 tool updates
v0.1.2- First observed
explain_why - First observed
recommend_for_project - First observed
recommend_next
TDQS
Scored across 4 tools
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.
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.
Four tools cover the essential recommendation scenarios without redundancy. The count is well-scoped for the server's purpose.
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
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