Algorithm Finder MCP server

algorithm-finder

Describe a computational problem and get back the algorithms that fit, why they fit, the bounds cited to the page, and where to get an implementation.

Part of Pipeworx — an MCP gateway connecting AI agents to 1743+ live data sources. This is an independent, unofficial integration — not affiliated with, endorsed by, or published by the upstream provider.

By Mojibake, creators of Pipeworx.

Ask it "all-pairs shortest paths" and it does not guess. It looks up the textbook answer (Floyd-Warshall, Johnson's, the precondition that decides between them, cited to the exact page of CLRS), checks the last five years of arXiv and Hugging Face Papers for anything newer, and tells you what it does not know yet. No model runs in the request path. Every fact is either a citation or a live source, never a guess.

Three things it actually returned

These are real calls against the live pack, trimmed for length. Nothing below is invented.

1. "What solves all-pairs shortest paths?"

algorithm_find({ problem: "all-pairs shortest paths", want: "both" })

Two methods, correctly separated by the constraint that actually decides between them:

{
  "name": "Floyd-Warshall algorithm",
  "preconditions": ["Negative edge weights are allowed, but there must be no negative-weight cycle."],
  "guarantee": "exact",
  "bounds": [
    { "quantity": "time", "bound": "Theta(V^3)", "kind": "worst_case",
      "cite": "CLRS4 ch. 23.2 p. pdf:851" }
  ],
  "when_to_use": "All-pairs shortest paths on dense graphs, or whenever its simple triple-loop implementation (no priority queue needed) is worth the cubic cost.",
  "when_not": "On sparse graphs, Johnson's algorithm has a better asymptotic bound.",
  "alternatives": ["Johnson's algorithm", "Dijkstra's algorithm", "Bellman-Ford algorithm"]
},
{
  "name": "Johnson's algorithm",
  "preconditions": ["No negative-weight cycle (checked by the initial Bellman-Ford run)."],
  "guarantee": "exact",
  "bounds": [
    { "quantity": "time", "bound": "O(V^2 lg V + VE)", "kind": "worst_case",
      "cite": "CLRS4 ch. 23.3 p. pdf:857" }
  ],
  "when_to_use": "All-pairs shortest paths on sparse graphs with some negative edge weights -- asymptotically faster there than Floyd-Warshall's Theta(V^3)."
}

It also names the open questions that would change the answer, before you ask:

"open_questions": [
  "Can any edge weight be negative? (Dijkstra requires non-negative weights; Bellman-Ford does not.)",
  "One source, or every pair of vertices?",
  "Are capacities integral? Is there a cost per unit of flow to minimise as well?"
]

...plus five recent, dated papers on the "novel" side, including Warm-Starting All-Pairs Shortest Paths with Predictions (arXiv, 2026) and Fast 2-Approximate All-Pairs Shortest Paths (arXiv, 2023).

2. "What's new in approximate nearest neighbor search?"

algorithm_find({ problem: "approximate nearest neighbor search", want: "novel" })

This is the pure research leg, no textbook entry involved. It does not just list papers; it clusters method names across them so a recurring name reads as a real answer rather than a title you have to notice yourself:

"novel_methods": [
  {
    "name": "HNSW",
    "mentions": 8,
    "first_seen": 2022,
    "last_seen": 2026,
    "example_paper": {
      "title": "From HNSW to Information-Theoretic Binarization: Rethinking the Architecture of Scalable Vector Search",
      "date": "2025-12-16T23:24:37.000Z",
      "url": "https://huggingface.co/papers/2601.11557"
    }
  },
  { "name": "NSG", "mentions": 6, "first_seen": 2017, "last_seen": 2025 },
  { "name": "SPANN", "mentions": 2, "first_seen": 2021, "last_seen": 2025 }
]

HNSW surfaces on its own, from the research, with the count and date range that make it a real signal rather than a single paper's opinion.

3. "CP-SAT, MIP or a flow formulation for fair shift scheduling?"

Shortened. The full README is on GitHub.