arquimedes-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| arquimedes_get_statsA | Summarize what the Arquimedes math problem database holds: 1,038,963 problems across 9 datasets, with problem counts by dataset, category, and competition, and written-solution counts by kind. Call it first to learn which category, dataset, and competition values exist before filtering arquimedes_search_problems or arquimedes_sample_problems. Takes no arguments. |
| arquimedes_search_problemsA | Find problems by keyword in 1,038,963 competition and olympiad math problems. Full-text search returns problems whose statement or title contains all the query words: a topic, a theorem name such as 'Cauchy-Schwarz', or a LaTeX fragment. Optional filters narrow by category, dataset, competition, and Mohs difficulty (1.0 to 10.0). Results are summaries (id, title, competition, year, difficulty, answer, statement) without solutions; pass an id to arquimedes_get_problem for solutions and hints. To get random problems instead of keyword matches, use arquimedes_sample_problems. |
| arquimedes_sample_problemsA | Draw random problems from 1,038,963 competition and olympiad math problems, uniformly among those matching optional filters: category, dataset, competition, contest year, Mohs difficulty range (1.0 to 10.0), multiple-choice only, or with a diagram. Use it to build practice sets, mock contests, or evaluation samples; pass the same seed to repeat a draw. Results are summaries without solutions; pass an id to arquimedes_get_problem for solutions. To find problems about a specific topic or keyword, use arquimedes_search_problems. |
| arquimedes_get_problemA | Fetch one problem by its id, as returned by arquimedes_search_problems or arquimedes_sample_problems (e.g. 'prob.ahsme-1950-p10', 'prob.numina-000369'). Returns the full record: LaTeX statement, answer choices, answer, written solutions with progressive hints, difficulty, source, and diagram references. Ids cannot be guessed; an unknown id returns an error, so discover ids with search or sample first. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| math-practice-set | Generate a calibrated math competition practice set with hints and solutions. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| Arquimedes Summary Index | Global problem counts by dataset, category, competition, and solution kind. |
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: get_stats (metadata survey), search_problems (keyword lookup), get_problem (fetch by id), and sample_problems (random draw). Descriptions explicitly cross-reference each other, telling the agent when to prefer search over sample and vice versa. There is no meaningful overlap that could cause misselection.
All four tools follow a uniform arquimedes_verb_noun pattern (get_stats, search_problems, get_problem, sample_problems) in consistent snake_case. Verb choice maps cleanly to action, and the server prefix is applied uniformly. No convention mixing.
Four tools is compact but each earns its place in a read-only database workflow: discover facets, search, retrieve, and sample. Nothing feels redundant or padded. The scope (browsing a problem corpus) is fully served without bloat.
The surface covers the core read lifecycle: facet discovery via stats, keyword search with filters, id-based full retrieval including solutions/hints, and random sampling. Minor gaps exist (e.g., no bulk multi-id fetch or explicit pagination controls), but these are workarounds an agent can handle. No single-purpose dead ends.