eatmundo

Server Details

Cross-cuisine dish equivalence with shared-ingredient evidence: 1,956 dishes, 26 cuisines.

If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.

Status: Healthy
Last Tested: 2026-09-16 17:43
Transport: Streamable HTTP
URL

TDQS

A4.4/5.0

Scored 2026-09-16 14:36 across 5 tools

Disambiguation5/5

Each tool targets a distinct action: search, fetch recipe, compare two dishes, find equivalents, and plan a menu. There is no overlap in purpose, so an agent can easily select the correct tool.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with lowercase and underscores (e.g., search_dishes, get_recipe). This makes the set highly predictable and easy to navigate.

Tool Count5/5

With only 5 tools, the server is tightly scoped to its culinary exploration and planning domain. Each tool has a clear role, and no tool feels extraneous or missing.

Completeness5/5

The tool surface covers the core workflows: finding dishes, retrieving full recipes, comparing dishes, discovering cross-cuisine equivalents, and generating weekly menus. This is a complete set for the stated purpose, with no obvious dead ends.

Available Tools

5 tools

compare_dishesCompare two dishesA

Read-onlyIdempotent

Inspect

Compare two dishes ingredient by ingredient: shared items, what is unique to each, a match score, and - for the 848 pairs that have one - an editorial comparison paragraph in the requested language. Works for any two dishes in the corpus, whether or not they are an edge in the equivalence graph.

ParametersJSON Schema

Name	Required	Description	Default
`a`	Yes	First dish: slug or name.
`b`	Yes	Second dish: slug or name.
`lang`	No	Language for titles, ingredient names and URLs.	en

Output Schema

ParametersJSON Schema

Name	Required	Description
`a`	Yes
`b`	Yes
`notes`	Yes
`score`	Yes
`only_a`	Yes
`only_b`	Yes
`paragraph`	Yes
`same_form`	Yes
`attribution`	Yes
`compare_url`	Yes
`score_basis`	Yes
`stored_score`	Yes
`is_graph_edge`	Yes
`below_threshold`	Yes
`excluded_generics`	Yes	Pantry staples deliberately excluded from BOTH the score and the ingredient lists above, because they match almost everything (flour/water/oil/salt/yeast for food, water/salt/sugar for drinks). They are present in the dishes - they are just not evidence of similarity.
`shared_ingredients`	Yes

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the description only needs to add behavioral color: it lists the exact comparison facets, mentions that only 848 pairs have the editorial paragraph, and notes the language parameter. This adds meaning beyond the annotations without contradicting them.

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 tight sentences with no filler. The first sentence front-loads the verb, resource, and output list; the second clarifies scope with an exact note about the equivalence graph and the 848-pair limitation. Every clause earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Along with a 100%-covered schema and an output schema, the description is effectively complete for invocation: the input semantics are covered, the result is described, and the nonzero-analytics caveat is disclosed. A minor gap is not explaining behavior when a dish is not found, but that is not central to selecting and invoking the 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 100%, so the parameters are fully documented in the schema. The description adds a small amount of semantics by mentioning 'in the requested language,' which maps to lang, but does not meaningfully extend the meaning of the a or b parameters. A baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action ('Compare two dishes ingredient by ingredient') and lists concrete outputs: shared items, unique items, match score, and an optional editorial paragraph in the requested language. It also distinguishes itself from the likely alternative find_similar_dishes by noting it works for any two dishes in the corpus, regardless of the equivalence graph.

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 clearly implies the use case, but it does not explicitly say when to prefer this over siblings like find_similar_dishes, search_dishes, or get_recipe. It explains that it covers arbitrary pairs, so an agent can infer the main scenario, but there are no when-not-to-use or alternative-routing directions.

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

find_similar_dishesFind similar dishesA

Read-onlyIdempotent

Inspect

Find dishes from other cuisines that are equivalent to a given dish, from eatmundo's scored equivalence graph (11,467 edges over 1,956 recipes from 26 countries). Every edge is evidence-based - shared canonical ingredients - not a language-model guess, and every edge crosses country lines. Use it for "what is the Greek counterpart of lahmacun, and why".

ParametersJSON Schema

Name	Required	Description	Default
`dish`	Yes	Dish to start from: an eatmundo slug (e.g. "lahmacun") or a plain name ("rice pudding"). Names are resolved through site search; check resolved_by in the result.
`lang`	No	Language for titles, ingredient names and URLs.	en
`limit`	No	How many matches to return, best score first. The default of 5 is enough to see whether a cuisine has one clear counterpart or several competing ones.
`country`	No	Restrict matches to one cuisine, ISO 3166-1 alpha-2 (e.g. "GR"). 26 countries are covered.
`min_score`	No	Minimum match score. Defaults to 0 on purpose: 320 dishes only have below-threshold nearest matches, and a default of 50 would silently return nothing for them.

Output Schema

ParametersJSON Schema

Name	Required	Description
`notes`	Yes
`source`	Yes
`matches`	Yes
`attribution`	Yes
`resolved_by`	Yes
`score_basis`	Yes
`total_matches`	Yes

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds meaningful behavioral context: results come from a scored equivalence graph, edges are based on shared canonical ingredients rather than language-model guesses, and all edges cross country lines. This helps the agent trust and interpret the output beyond what annotations convey.

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?

Three focused sentences, each earning its place: the first states the core function, the second establishes provenance and trustworthiness, and the third gives a concrete usage prompt. The content is front-loaded and free of fluff.

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?

For a read-only lookup with a high-quality input schema and an output schema available, the description covers everything needed for correct selection and invocation. It explains the data source, scope, evidence basis, and typical use case without redundantly repeating schema details.

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

Parameters3/5

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

Schema description coverage is 100%, and the parameter descriptions themselves are detailed (including min_score's non-obvious default rationale). The tool description adds no parameter-specific meaning beyond the overall equivalence-graph context, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action ('Find dishes from other cuisines that are equivalent to a given dish') tied to a concrete resource ('eatmundo's scored equivalence graph'). It clearly differentiates this tool from siblings by emphasizing cross-country equivalence and evidence-based edges, so an agent can distinguish it from compare_dishes or search_dishes.

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 gives a concrete usage example ('what is the Greek counterpart of lahmacun') and implicitly scopes the tool to cross-cuisine comparisons. It does not explicitly name sibling tools or state when not to use it, but the intended use case is clear enough for confident selection.

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

get_recipeGet recipeA

Read-onlyIdempotent

Inspect

Full recipe for one dish: steps, FAQ, image credit, and ingredients with PARSED quantities (amount, unit, and a normalised base in grams or millilitres where the quantity is measurable). Count-based lines like "3 onions" carry a gram estimate only where a per-item weight is known.

ParametersJSON Schema

Name	Required	Description	Default
`lang`	No	Language for titles, ingredient names and URLs.	en
`slug`	Yes	Recipe slug in any of the 7 languages, or a dish name (resolved by search).

Output Schema

ParametersJSON Schema

Name	Required	Description
`faq`	Yes
`url`	Yes
`form`	Yes
`slug`	Yes
`image`	Yes
`notes`	Yes
`steps`	Yes
`title`	Yes
`country`	Yes
`calories`	Yes
`category`	Yes
`servings`	Yes
`attribution`	Yes
`description`	Yes
`ingredients`	Yes
`match_count`	Yes
`resolved_by`	Yes
`country_name`	Yes
`prep_time_minutes`	Yes

TDQS

A4.5/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so safety is covered. The description adds substantial behavioral detail beyond that: it explains that quantities are PARSED with amount, unit, and a normalized base in grams/millilitres, and how count-based lines are handled with gram estimates. This informs the agent about the output structure and precision, which is valuable context not present in annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

The description is two sentences, front-loaded with the core purpose ('Full recipe for one dish') and then elaborates on the key detail of parsed quantities. Every clause adds value—no redundancy or filler. It is concise and well-structured.

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?

With an output schema present, the description needn't explain return values in full, but it adds valuable context about the normalized quantities and gram estimates. The tool is simple (get recipe by slug) and the description covers the essential behavior. There are no obvious missing pieces an agent would need to call it correctly.

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

Parameters3/5

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

Schema description coverage is 100%: both 'lang' (with enum and default) and 'slug' (with explanation of language and resolution by search) are fully documented in the schema. The tool description adds no additional parameter semantics, so a baseline 3 is appropriate. The description doesn't need to compensate for any schema gaps.

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?

States a specific verb and resource: get the full recipe for a single dish, listing the exact components (steps, FAQ, image credit, ingredients with parsed quantities). This clearly distinguishes it from siblings like search_dishes (finding dishes) and compare_dishes (comparing).

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: when you have a slug or dish name and need a complete recipe, use this tool. It doesn't explicitly contrast with alternatives or state when not to use it, but the sibling names and the description's focus on a single dish give enough contextual cue. A clear 'use this for a specific dish' statement is missing.

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

search_dishesSearch dishesA

Read-onlyIdempotent

Inspect

Search 1,956 dishes from 26 cuisines by text, country, category, preparation technique, or canonical ingredient. Every ingredient in the corpus is linked to a canonical entity, so an ingredient filter also catches translations and spelling variants. Use it to get a slug for the other tools.

ParametersJSON Schema

Name	Required	Description	Default
`form`	No	Preparation technique. One of: soup, stew, stir-fry, grill-roast, fritter, dumpling, stuffed-vegetable, flatbread-pastry, noodle, rice, cold-salad, dip-spread, pickle-ferment, egg, sandwich-wrap, porridge-mash, casserole-bake, cured-preserved, composed-plate, meat-salad, pancake-batter, marinated-cooked, syrup-pastry, fried-dough-sweet, cake, cookie, custard-pudding, sweet-pie-tart, fruit-dessert, frozen, confection, sweet-porridge, glutinous-dumpling, sweet-bread, layered-assembly, filled-pastry, sweet-pancake, starch-gel, hot-infusion, cold-cooler, juice, smoothie-shake, hot-milk-drink, fermented-drink, alcoholic-mixed, alcoholic-brew.
`lang`	No	Language for titles, ingredient names and URLs.	en
`limit`	No	Results per page (max 25).
`query`	No	Free text; needs at least 2 characters to take effect.
`offset`	No	Rows to skip for paging. Pass back the `next_offset` from the previous response; it is null when there is no further page.
`country`	No	ISO 3166-1 alpha-2 cuisine filter.
`category`	No	Dish type. DRINK covers both alcoholic and non-alcoholic; DESSERT is separate from FOOD, so a query for a sweet dish returns nothing under FOOD.
`ingredients`	No	Canonical ingredient names (e.g. ["tomato","garlic"]). A dish matches if it has ANY of them, but results are ranked by how many it has. Unrecognised names are reported back, not ignored.

Output Schema

ParametersJSON Schema

Name	Required	Description
`items`	Yes
`notes`	Yes
`total`	Yes
`offset`	Yes
`attribution`	Yes
`next_offset`	Yes
`unresolved_ingredients`	Yes

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds valuable behavioral context: canonical ingredient linking catches translations/variants, unrecognized ingredients are reported back, and category semantics (DRINK covers both, DESSERT separate from FOOD) are disclosed. This goes beyond the annotations and helps the agent predict behavior.

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 with zero waste. The first sentence front-loads the core purpose and scope, and the second adds the key behavioral nuance and the practical use case (getting a slug). Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a search tool with 8 parameters, 100% schema coverage, and an output schema, the description is largely complete. It covers the main search dimensions, the canonical ingredient behavior, and the slug use case. It doesn't mention pagination or the next_offset field, but the schema already documents that, so the description need not repeat it. A 4 is fair given the richness of the schema and annotations.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all 8 parameters. The description adds a few semantic details not in the schema: the canonical ingredient matching behavior and the category note about DESSERT vs FOOD. However, most parameter meaning is already in the schema, so the description's added value is moderate. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Search'), a precise resource ('1,956 dishes from 26 cuisines'), and enumerates the search dimensions (text, country, category, preparation technique, canonical ingredient). It also distinguishes itself from siblings by noting it returns a slug for other tools, which is a clear differentiator.

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 gives clear context on when to use this tool: to search dishes and get a slug for other tools. It does not explicitly name alternatives or state when not to use it, but the sibling list and the slug mention imply its role as the entry point. A 4 is appropriate because the usage context is clear but exclusions are not explicit.

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

weekly_menuBuild a weekly menuA

Read-only

Inspect

Build a menu of up to 7 dinners, either one cuisine per day ("world") or all from one country. Honours diet exclusions (pork, beef, meat, fish, dairy, egg, alcohol), vegetarian/vegan/pescatarian requirements, a prep-time ceiling and a budget mode that reuses ingredients. When a constraint cannot be met it is relaxed and the relaxation is reported, never hidden.

ParametersJSON Schema

Name	Required	Description	Default
`days`	No	How many days to plan, 1-7.
`lang`	No	Language for titles, ingredient names and URLs.	en
`mode`	No	"world" = a different cuisine each day; "single" = one country.	world
`seed`	No	Same seed + same options = same menu.
`budget`	No	Favour weeks that reuse ingredients across days.
`country`	No	Required when mode is "single".
`exclude`	No	Ingredient attributes to avoid entirely: pork, beef, meat, fish, dairy, egg, alcohol.
`require`	No	Diet every dish must satisfy. This is a WHITELIST and differs from `exclude`, which only removes single ingredient attributes: "vegan" is not the same as excluding meat.
`time_limit`	No	Max prep time in minutes; closed set (30/45/60). The generator relaxes it automatically if too few cuisines qualify, and reports that in relaxations.
`include_extras`	No	Add 2 desserts and 2 drinks (never alcoholic).
`exclude_countries`	No	Cuisines to keep out of the plan, ISO 3166-1 alpha-2. Only meaningful when mode is "world"; excluding too many leaves the generator with too few cuisines to fill the week.

Output Schema

ParametersJSON Schema

Name	Required	Description
`days`	Yes
`notes`	Yes
`stats`	Yes
`drinks`	Yes
`desserts`	Yes
`attribution`	Yes
`relaxations`	Yes
`shopping_list`	Yes
`pantry_assumed`	Yes

TDQS

A4.2/5.0

Behavior4/5

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

Beyond the readOnlyHint annotation, the description adds valuable behavioral context: it states that constraint relaxation is always reported and never hidden, and explains budget mode reuses ingredients. There is no contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

Three sentences with zero filler: first sentence states the core purpose and modes, second summarizes constraints, third guarantees transparency about relaxations. The most important information is front-loaded.

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 an 11-parameter tool with an output schema, the description provides a sufficient high-level model—modes, key constraints, and relaxation behavior. It does not mention language, seed, include_extras, or exclude_countries, but those are fully documented in the schema, so this is not a material gap.

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 100%, so the baseline is 3. The description adds integrative meaning by explaining how diet exclusions, prep-time ceiling, and budget mode combine, clarifying the semantics of exclude, require, time_limit, and budget parameters. It does not enumerate each parameter, but the schema already covers that.

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 a specific verb and resource: 'Build a menu of up to 7 dinners', with explicit modes ('world' or 'single'). This clearly differentiates it from dish-level siblings like search_dishes and get_recipe, which operate on individual dishes rather than a weekly plan.

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?

Usage is implied by the description—this tool builds menus—but there is no explicit when-to-use or when-not-to-use guidance, nor any reference to alternative sibling tools. An agent must infer that a weekly menu request maps to this tool, which is reasonable but not explicitly guided.

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.

3 tool updatesSep 16, 2026

Changedfind_similar_dishes1 field changed

addedInput schema / properties / limit / description

Added value: +"How many matches to return, best score first. The default of 5 is enough to see whether a cuisine has one clear counterpart or several competing ones."

Changedsearch_dishes3 fields changed

addedInput schema / properties / category / description

Added value: +"Dish type. DRINK covers both alcoholic and non-alcoholic; DESSERT is separate from FOOD, so a query for a sweet dish returns nothing under FOOD."

addedInput schema / properties / limit / description
```
Added value: +"Results per page (max 25)."
```

addedInput schema / properties / offset / description

Added value: +"Rows to skip for paging. Pass back the `next_offset` from the previous response; it is null when there is no further page."

Changedweekly_menu3 fields changed

addedInput schema / properties / days / description
```
Added value: +"How many days to plan, 1-7."
```

addedInput schema / properties / exclude_countries / description

Added value: +"Cuisines to keep out of the plan, ISO 3166-1 alpha-2. Only meaningful when mode is \"world\"; excluding too many leaves the generator with too few cuisines to fill the week."

addedInput schema / properties / require / description

Added value: +"Diet every dish must satisfy. This is a WHITELIST and differs from `exclude`, which only removes single ingredient attributes: \"vegan\" is not the same as excluding meat."

1 tool updateSep 10, 2026

Changedcompare_dishes2 fields changed

addedOutput schema / properties / excluded_generics

Added value: +{
+  "description": "Pantry staples deliberately excluded from BOTH the score and the ingredient lists above, because they match almost everything (flour/water/oil/salt/yeast for food, water/salt/sugar for drinks). They are present in the dishes - they are just not evidence of similarity.",
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}

changedOutput schema / required

Previous value: -[
-  "a",
-  "b",
-  "score",
-  "score_basis",
-  "is_graph_edge",
-  "stored_score",
-  "below_threshold",
-  "same_form",
-  "shared_ingredients",
-  "only_a",
-  "only_b",
-  "paragraph",
-  "compare_url",
-  "notes",
-  "attribution"
-]New value: +[
+  "a",
+  "b",
+  "score",
+  "score_basis",
+  "is_graph_edge",
+  "stored_score",
+  "below_threshold",
+  "same_form",
+  "shared_ingredients",
+  "only_a",
+  "only_b",
+  "excluded_generics",
+  "paragraph",
+  "compare_url",
+  "notes",
+  "attribution"
+]

5 tool updatesSep 10, 2026
- First observedcompare_dishes
- First observedfind_similar_dishes
- First observedget_recipe
- First observedsearch_dishes
- First observedweekly_menu

Related MCP Connectors

Symmap A
io.github.pipeworx-io
SymMap v2 — Traditional Chinese Medicine association graph from symmap.org
Biology & Medicine Research & Data Open Data
381
nutrition A
org.heathy
Nutrition for 910 plant foods across 11 national datasets, plus GB/EU & US claim checking
Health & Wellness Open Data Research & Data
81
Conceptnet A
io.github.pipeworx-io
ConceptNet MCP — open multilingual word/phrase relation graph
Knowledge & Memory AI & Machine Learning
342
Cantara wiki — community knowledge web A
no.cantara
Norwegian open-source community wiki (2,290 articles, 2008-2022) as a signed KCP knowledge web.
Knowledge & Memory Open Data
52

Related MCP Servers

menus-mcp
Search Knowledge & Memory
spyyps
F
license
Not graded
quality
C
maintenance
Recommends dishes from a global cuisine knowledge base based on user preferences (keywords, cuisine, price, etc.) via weighted random selection with bias correction and diversity.
Updated 3 months ago (2026-06-16 06:21 UTC)
-
unfragile-mcp-server
Search AI & Machine Learning Developer Tools
Savirinc
A
license
A
quality
D
maintenance
The match graph for AI. Search 100K+ capabilities across 13K+ AI artifacts.
Updated 4 months ago (2026-05-27 10:21 UTC)
10
41 npm
2
MIT
grant-mcp
Research & Data Government Data Search
vertical-mcp
A
license
B
quality
D
maintenance
MCP server for searching research grants across NSF (US), ERC (EU), and KRF/NRF (Korea) via a unified interface. NIH excluded—covered by existing connectors.
Updated 5 months ago (2026-04-21 08:25 UTC)
3
7 npm
MIT
korean-vocational-learning-map
Education & Learning Tools Open Data
raphysicst-create
A
license
Not graded
quality
B
maintenance
MCP server providing a local learning graph for the Korean vocational high school curriculum (2022 revised), including 8,425 achievement standards, topics, and 10 tools for searching standards, topics, prerequisites, and learning roadmaps.
Updated 23 days ago (2026-08-25 02:16 UTC)
27 npm
2
MIT

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?