What can you do with this server?

This server lets an AI assistant query a curated, offline snapshot of 1,212 generative AI tools. * Search tools by free text and filters: type, ecosystem, origin, capabilities, and curator picks. * Retrieve full details for any tool by ID. * List all categories with record counts to build valid filters. * Compare 2–4 tools side by side on the same fields. * Runs without API keys, network access, or a database—data ships with the repo.

How do I use genai-solutions-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@genai-solutions-mcp Find open-source video editing tools that run locally" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

de en es ja ko ru zh

genai-solutions-mcp

by tewfiq

Overview Schema Related Servers Score Discussions

TypeScript

Local

genai-solutions-mcp

An MCP server that exposes a curated database of generative AI tools — 1,212 records I have maintained in Notion since May 2024 — as four tools an AI assistant can query directly.

Instead of asking a model what AI tools exist and getting a plausible but stale answer, you ask it to search a dataset that has a person behind it.

> Which open-source tools in here run locally and have a CLI?
> Compare Firecrawl and the other scraping options.

Install

npm install
npm run build
npm start

No API key, no network, no database. The dataset ships with the repo.

{
  "mcpServers": {
    "genai-solutions": {
      "command": "node",
      "args": ["/absolute/path/to/genai-solutions-mcp/dist/src/index.js"]
    }
  }
}

Related MCP server: disvr

Tools

Tool	Purpose
`search_solutions`	Free-text + filters (type, ecosystem, capabilities, origin, picks)
`get_solution`	Full record for one id
`list_categories`	Every category with its record count
`compare_solutions`	2–4 records aligned on the same fields

Design decisions

A committed snapshot, not a live Notion proxy. The obvious design is to call the Notion API on every tool call. It is also the one that makes the repo useless to everyone but me: you would need my token and my database. Instead, scripts/sync-notion.ts exports Notion to data/solutions.json, which is versioned here, and the server only reads that file. The trade-off is freshness — the data is as current as the last sync — against a server that anyone can clone and run in fifteen seconds, with no credentials, no network dependency at call time, and no rate limit. The underlying data changes weekly at most, so freshness is the cheaper thing to give up.

Substring search, not embeddings. 1,212 records is a sub-millisecond linear scan. A vector index would add an embedding step to the sync, a model dependency, non-deterministic results, and an index to keep consistent with the snapshot — in exchange for semantic recall on a corpus where the useful queries are mostly names and categories. If the dataset grows an order of magnitude or the summaries get longer, this is the first thing to revisit.

Search returns a projection, not full records. search_solutions returns only id, name, type and url. Returning complete records for a 20-result query would spend a large amount of the agent's context on fields it usually does not need; get_solution is there for when it does.

A property whitelist in the sync, not a blacklist. The Notion database contains internal workflow state and attachments that have no business being public. scripts/sync-notion.ts names the properties it exports, so adding a private column in Notion later cannot silently leak it here.

Known limitations

Notion's Type is a single select, so each tool has exactly one category even when two would fit.
Category values were entered by hand over two years and are uneven; the sync folds casing duplicates but does not merge near-synonyms.
No relevance ranking beyond field-weighted substring matching.
Attribute tagging is uneven. Local and Open Source were applied at different periods with different habits, and they overlap on only one record even though many tools qualify for both. Filters are honest about what is tagged, not about what is true — a property this dataset shares with most real internal databases.
The corpus starts in May 2024. A dozen records carry an earlier date: those are tools I had found and tested before, backdated by hand when I entered them.
Node resolution depends on the host environment. The server was observed starting under two different Node installations on the same machine depending on the launching context. Pin the runtime path in your client config if that matters to you.

Maintenance

Both scripts are maintainer-only and need Notion credentials.

Sync the snapshot:

NOTION_TOKEN=… NOTION_DATA_SOURCE_ID=… npm run sync
git diff data/solutions.json   # read it before committing

Backfill missing capture dates. The Date property is the day I captured a tool, entered by hand, so it is occasionally left empty. This fills the gaps with the page's creation time, which is the closest available proxy. It runs as a dry run by default — read the output before applying.

NOTION_TOKEN=… npx tsx scripts/backfill-dates.ts
NOTION_TOKEN=… npx tsx scripts/backfill-dates.ts --apply

License

MIT

Available Tools

4 tools

compare_solutionsCompare solutionsB

Return 2 to 4 solutions side by side on the same fields.

ParametersJSON Schema

Name	Required	Description	Default
`ids`	Yes

TDQS

B3.3/5.0

Behavior2/5

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

No annotations are provided, so the description must carry the full burden of disclosing behavioral traits. The description only mentions returning solutions side by side, but does not state whether this tool is read-only, any side effects, or constraints like required permissions. It lacks transparency beyond the basic function.

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 uses active voice and front-loads the key information (return 2 to 4 solutions). No unnecessary words.

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 that there is no output schema and no annotations, the description is minimal. It tells what the tool does and the range of solutions, but does not describe the output format, any sorting or ordering, or related constraints like required fields for comparison. It is adequate but incomplete.

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

Parameters3/5

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

The schema describes the only parameter 'ids' with min/max items, but the description adds context that the output is a side-by-side comparison on the same fields. Since schema coverage is 0%, the description partially compensates by explaining the purpose, but does not elaborate on the 'ids' parameter meaning or format.

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 returns 2 to 4 solutions side by side on the same fields, which provides a specific verb ('return') and resource ('solutions'). It distinguishes from siblings like 'search_solutions' or 'get_solution' by emphasizing comparison.

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 that this tool is for comparing multiple solutions, but it does not provide explicit guidance on when to use it versus alternatives like 'get_solution' or 'search_solutions'. No when-not-to-use or exclusion criteria are mentioned.

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

get_solutionGet one solutionA

Return the full record for a single solution by id.

ParametersJSON Schema

Name	Required	Description	Default
`id`	Yes

TDQS

A3.7/5.0

Behavior3/5

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

Since no annotations are provided, the description bears the full burden of behavioral disclosure. It confirms a read operation ('Return the full record') without indicating idempotency, rate limits, or error behavior. It does not state whether the returned record is guaranteed fresh or cached, nor whether authentication is needed. This is adequate 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence (14 words) that efficiently captures purpose, resource, and method. Every word is relevant. No wasted phrasing. Front-loaded with the action 'Return the full record' – ideal for scanning.

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 (1 required param, no nested objects, no output schema, no annotations), the description provides sufficient functional purpose. However, it lacks mention of what happens if the id is invalid (e.g., 404), or whether the response includes nested objects like related items. The absence of an output schema means the description should hint at the 'full record' structure, but it does not enumerate fields. It is just adequate for a simple retrieval tool.

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

Parameters3/5

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

Schema description coverage is 0%, meaning the description must compensate. The description mentions 'by id,' which maps to the required 'id' parameter, adding basic context. However, it does not clarify that id is a string (schema indicates type), nor does it provide format guidance (e.g., UUID vs plain string). For a single required param, this is borderline acceptable but lacks helpful detail 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 'Return the full record for a single solution by id,' specifying the verb (return) and resource (full record for solution) and the identifying method (by id). It distinguishes from list_categories and search_solutions (which retrieve multiple) and compare_solutions (comparison), but could be more explicit that this is the singular retrieval endpoint.

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 use when you need a complete record of a known solution, contrasting with search_solutions (which returns summaries/partial data) and list_categories (which returns categories, not solutions). However, no explicit 'when not to use' guidance is given, though the sibling context clarifies alternatives. The lack of mention about requiring the ID beforehand is a minor gap.

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

list_categoriesList categoriesA

Return every category present in the database with its record count, so filters can be built against real values rather than guesses.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A4.3/5.0

Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns every category with its record count, which is a clear behavioral trait. It does not mention performance, data freshness, or side effects, but for a simple read-only list tool, the description is adequate.

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 key action ('Return every category') and includes the purpose. Every word earns its place with no wasted content.

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 no parameters, no output schema, and no annotations, the description is relatively complete for a simple list tool. However, it could be improved by mentioning the response format (e.g., whether categories are sorted) or any limits. The explanation of purpose is helpful, but the lack of output schema details leaves some ambiguity.

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?

There are no parameters, so schema description coverage is trivially 100%. The description adds no parameter information, but with zero parameters, a baseline score of 4 is appropriate as the description already provides value beyond the 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 states 'Return every category' with a specific verb and resource, and explains the purpose ('so filters can be built against real values rather than guesses'). This clearly distinguishes it from sibling tools which deal with solutions, not categories.

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 when to use this tool: when building filters based on real category values. It provides context but does not explicitly exclude alternatives or state when not to use it. However, siblings are clearly different (solutions), so the guidance is sufficient.

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

search_solutionsSearch GenAI solutionsA

Search a curated database of generative AI tools by free text and filters. Returns a short projection (id, name, type, url); call get_solution with an id for the full record.

ParametersJSON Schema

Name	Required	Description
`type`	No	Category — call list_categories for valid values
`limit`	No	Default 20, max 100
`query`	No	Free text, matched against name
`origin`	No	All must match. E.g. France, EMEA, China, YC
`ecosystem`	No
`picksOnly`	No	Restrict to curator's picks
`capabilities`	No	All must match. E.g. API, Open Source, Local, Terminal, Installation, HF

TDQS

A4/5.0

Behavior3/5

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

With no annotations provided, the description takes on the full burden of behavioral disclosure. It explains the return shape (short projection) and directs to get_solution for full records. However, it omits behavioral traits such as read-only nature, rate limits, authentication requirements, pagination behavior (though limit param exists), result ordering, or what happens with empty results. This is a moderate disclosure but has gaps.

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 front-load the action and result, then provide a clear pointer to the sibling tool. Every sentence serves a distinct purpose, with no fluff or repetition. Ideal conciseness for a search tool.

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 tool with 7 optional parameters, no required fields, and no output schema, the description covers the main input mechanism (free text and filters), the output projection, and the follow-up sibling call. However, it does not explain the overall filtering logic (AND across parameters) or pagination, which are implicit from the limit parameter. Still, it is reasonably complete for standard usage.

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 high (86%), so the schema already provides strong param documentation. The description adds no additional parameter-level information beyond the schema (e.g., it doesn't explain how multiple filters combine or the meaning of limit's default). Given the high baseline, the description's value is neutral; it doesn't degrade but doesn't enhance.

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 verb 'Search' and the resource 'curated database of generative AI tools', and specifies the method 'by free text and filters'. It distinguishes itself from siblings by mentioning 'returns a short projection (id, name, type, url); call get_solution...' and implicitly from list_categories (for valid values of type) and compare_solutions (not mentioned but different purpose).

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 tells the agent when to use get_solution ('for the full record') after a search, which provides a clear alternative. However, it does not explicitly state when to avoid search_solutions or when to use list_categories or compare_solutions, leaving some ambiguity for related sibling tools.

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.

4 tool updatesv0.1.0Aug 18, 2026
- First observedcompare_solutions
- First observedget_solution
- First observedlist_categories
- First observedsearch_solutions

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A4/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: search for filtering, get for full details, list categories for filter options, and compare for side-by-side comparison. No overlap exists.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (search_solutions, get_solution, list_categories, compare_solutions), making them predictable and easy to understand.

Tool Count5/5

With four tools, the server is well-scoped for a curated database of generative AI solutions. Each tool earns its place, covering search, retrieval, category listing, and comparison without unnecessary bloat.

Completeness5/5

The tool set covers the core read-only operations needed for a solutions database: discovery (search), detail retrieval (get), filter exploration (list_categories), and comparison. No obvious gaps—the domain is fully addressed.

Maintenance

ActivityMaintained

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

Related Servers

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Remote MCP A
com.remote-mcp.mcp
The Remote MCP server acts as a standardized bridge between LLM applications (like Claude, ChatGPT, and Cursor) and external services, enabling AI agents to access external tools and resources. Its primary capability is providing a centralized search tool to discover other MCP servers and their respective tools. Unlike local implementations, it runs remotely with OAuth authentication and permission controls for security.
Developer Tools Search Agent Orchestration
13
Anagent A
io.github.jaytahir
A registry of AI agent tools — MCP servers, APIs, CLIs, SDKs — kept current by automated ingestion.
Agent Orchestration Developer Tools
21
MCP Aggregation Gateway — 22k+ MCP search & call A
cn.pianam.mcp
One AI endpoint to search and call 22k+ MCP servers; 50+ hosted tools work instantly, no key.
Agent Orchestration Search
31
MCPExplorer A
io.github.mcpexplorer
Search, vet & assemble MCP servers from your agent: verified tools, risk labels, and trust scores.
Developer Tools Search Agent Orchestration
1421

Related MCP Servers

MCP-MCP
Developer Tools Agent Orchestration
wojtyniak
A
license
A
quality
B
maintenance
A Meta-MCP Server that acts as a tool discovery service, helping AI assistants find appropriate MCP servers from a database of 800+ servers when they need capabilities that aren't currently available.
Updated 2 months ago (2026-06-26 19:39 UTC)
1
23
MIT
disvr
Search RAG Systems Developer Tools
Svanik-yan
A
license
Not graded
quality
F
maintenance
Tool search engine for AI agents. One API call to discover the best MCP server for any task. 900+ services indexed with 4-dimensional value ranking.
Updated 5 months ago (2026-03-31 00:51 UTC)
MIT
Forage MCP Server
Autonomous Agents Developer Tools
isaac-levine
A
license
Not graded
quality
D
maintenance
An MCP server that enables AI agents to automatically discover, install, and learn to use new tools without manual configuration.
Updated 7 months ago (2026-02-21 23:02 UTC)
52
16
MIT
SPURS MCP Server
Agent Orchestration Autonomous Agents
Universal-Standard
F
license
Not graded
quality
D
maintenance
Self-evolving MCP server that automatically discovers, generates, and registers new tools on demand using AI, enabling dynamic tool expansion without manual intervention.
Updated 5 months ago (2026-04-08 07:05 UTC)
-

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/tewfiq/genai-solutions-mcp'

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