Mathlas vs Math MCP Learning Server

verify_numeric

Airtight check that a closed-form expression equals a numeric value: independent sympy re-evaluation at higher precision, verified only on >= 20 agreeing digits. Use BEFORE asserting any numeric identity. Args: value (decimal string), closed_form (e.g. 'pi**2/6', 'zeta(3)').

verify_formal

Run the REAL Lean 4 kernel (NO LLM). Two modes: (1) pass `lean` (a full snippet, e.g. 'example : 2 + 2 = 4 := rfl') to typecheck it as-is; (2) pass `proof` to PROOF-CHECK — `statement` must then be the Lean 4 proposition and `proof` YOUR proof (term or 'by ...' tactic block); mathlas builds `theorem _mathlas_check : <statement> := <proof>` and the kernel returns proof_status VERIFIED_PROOF / REFUTED (kernel_error carries the kernel's exact complaint — use it to repair the proof and re-call) / UNDETERMINED (no toolchain / timeout / unresolvable import — honest, never fake). sorry/admit are REJECTED. mathlas never writes proofs, only checks them. Find declaration names first with search_formal_math. Args: statement, lean?, proof?.

applicability_checklist

Decompose a candidate theorem's statement into atomic preconditions + conclusion for YOU to verify one by one against your problem (catches misapplications like using a closed-interval theorem on an open interval). Use after search, before relying on any candidate. Args: candidate_statement (the result's statement text).

mapping_scaffold

Build the needs<->guarantees scaffold (structured questions + fill-in template) between your problem and a candidate result. Use when applicability is non-obvious and you want structure for the judgment (the judging is yours). Args: problem, candidate_statement.

conjecture_relation

Conjecture relations for a real constant — Ramanujan-Machine style: PSLQ over a rich basis + continued-fraction/recurrence search; every candidate numerically VERIFIED to >= 25 digits but NOT proved (provenance 'conjectured_relation'). Use when identify_constant returns UNIDENTIFIED. Args: value (decimal string, MANY digits), max_terms (default 16), cf_depth (default 200).

funsearch

Sandboxed program-search harness (FunSearch): action='evaluate' scores YOUR Python program for problem_id ('cap_set' or 'online_bin_packing') in a no-network/timeout/rlimit sandbox; action='register' stores a scored program in the MAP-Elites DB; action='status' returns the best programs + few-shot context for writing the next variant. Use to iteratively evolve programs — YOU are the generator, mathlas is the deterministic scorer. Args: action, problem_id, then program_src (evaluate/register), score + behavior (register), timeout_s (evaluate), top_k (status).

search_directive

Get a STRUCTURED web-search plan for a problem — arXiv query strings, sub-fields/categories, named results to look for, and which other mathlas tools to run; mathlas makes NO web call (YOU search, then feed results back via add_finding). Use when the local index missed. Args: problem (description).

add_finding

Ingest a web-found result into the live mathlas corpus so search_existing_math returns it immediately (provenance 'web_added'; BM25 always — no model load; full dense retrieval too if you pass dense_vec embedded in the served index's space). Use after web-searching per search_directive. Args: statement, slogan, source, optional name, optional dense_vec.