"How to find and troubleshoot a memory leak in software" matching MCP tools:

• how_munimji_helps

  HelloBooks AI Agents MCP Server

  Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.

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• memory_rename

  emem — Earth memory protocol

  Move (rename) a memory file from `old_path` to `new_path`. Both paths must stay under `/memories/`; `new_path` must not already exist. The file_cid is preserved (no re-sign) so the prior receipt still binds the bytes. Mirrors the `rename` verb in Anthropic's context-management-2025-06-27 memory tool spec. When to use: Call when the LLM wants to rename or move a memory file. Failure modes: source missing, destination already exists, path escapes `/memories/`.

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• govcon_search_contract_awards

  DataNexus MCP

  Search government contract awards by keyword, agency, and date range. keyword: Contract scope e.g. "cybersecurity software". agency: Awarding agency e.g. "Department of Defense". Optional. date_from: Earliest award date ISO 8601 e.g. "2024-01-31". Optional. jurisdiction: "US", "EU", or "UK". Default "US". Returns: award amounts, recipient vendors, NAICS codes, award dates. Use govcon_fetch_vendor_contract_history for all contracts by a specific vendor. Use govcon_fetch_open_solicitations for active bids, not past awards. Source: USASpending.gov + SAM.gov. 4-hour cache. Example: search_contract_awards(keyword="cybersecurity software", agency="Department of Defense")

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• emem_region_similarity

  emem — Earth memory protocol

  Answer 'how alike are these two places?' Mean-pool the 128-D GeoTessera embedding across each region's cells to get a centroid, then return the cosine similarity in [-1,1] (+1 = identical landscape, 0 = unrelated). Each region is {place} | {polygon_bbox} | {cells}. CPU-fetched embeddings — no GPU sidecar needed. Surfaces how many cells in each region actually carried a vector (coverage). When to use: Call to compare two areas at the level of overall land character (e.g. 'is this valley like that one?', 'find me somewhere that looks like X'). Degrades to a signed `inconclusive` (no number) when a region has no embedding-covered cells. For a single cell-to-cell vector cosine use `emem_compare`; for k-NN retrieval use `emem_find_similar`.

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• security_advisories

  dynamic-feed

  Recent software security advisories / CVEs — each with the affected package, vulnerable version range, the patched version that fixes it, severity, and CVSS score. Use this to check if a package has a recent advisory, or to get the latest critical CVEs. Pairs with software_version (is my stack current AND safe?). Newest first. Source: GitHub Advisory Database. Note: covers recently-published reviewed advisories, not the full historical CVE corpus. Envelope: this is an EVENT feed, so checked_at = when WE last refreshed the advisory store (freshness reflects how current our mirror is, NOT how long since the last CVE — a quiet stretch is not stale data). The newest advisory's own age is surfaced as latest_advisory_age_s. Args: query: match summary / package / CVE id / GHSA id. package: affected package name (e.g. lodash, requests, log4j). ecosystem: npm | pip | maven | go | rubygems | nuget | composer | rust | ... severity: low | moderate | high | critical. min_cvss: minimum CVSS score (0-10). limit: max results.

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• recall_nearby_memories

  space0

  Call this BEFORE you decide your next action: it grounds the choice in what you already know about THIS place instead of starting blind. Returns your OWN anchored memories within radiusM of your position (default 20m, max 64m). Or, when you pass regionId, every memory you anchored inside that labeled region (e.g. "my library"). Without `query` they come back nearest-first. With `query` (free text describing what you are trying to remember) radius-mode recall is ranked by a hybrid of semantic relevance to the query AND spatial proximity, plus recency, importance, and how recently the place was re-seen, so "what is relevant to what I am doing, near where I stand" is one call. Each entry: {memory_id, kind, event_text, importance, position, distance_m, occurred_at, region}. Only YOUR memories are ever returned, never another agent's. Anchor memories first with append_memory(space, position); pair with build + label_region to construct a navigable memory palace you can revisit and read back. Recall is occlusion-BLIND by default: you remember a memory in the next room even though a wall blocks sight, just as you know what is there without seeing it. Pass lineOfSightOnly:true for a perception-style question instead - restrict results to memories whose subject is currently VISIBLE from where you stand.

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• academy_welcome

  StudioMeyer Academy

  ALWAYS call this first when a user connects or asks what this is. Returns a short orientation for StudioMeyer Academy — a free 6-level 'Memory-First AI Operator' curriculum (Levels 1-3 fundamentals, 4-6 memory/MCP/multi-agent), plus playbooks and build recipes. Read it back to the user in their language and offer to start at their level.

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• how_to_give_feedback

  life. scored.

  How to suggest a better weight, a fresh source, or a new rule via GitHub, so improvements from many people aggregate in the open.

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• list_public_theses_by_user

  Valuein — SEC EDGAR Fundamentals & Smart-Money Data

  Return the PUBLIC theses + reputation aggregate for a user identified by Stripe customer_id. Used by the /[handle] profile page to render an analyst's track record. Only entries with visibility='public' are surfaced — private theses never leak. Reputation is correct/(correct+wrong) over graded theses; null when n < 5 (sample too small). Sample tier rejected; sp500+ only.

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• getting_started

  mrmarket.ai

  Onboarding tour for mrmarket.ai — call this FIRST in a fresh session, or any time the user asks "what can you do?" / "how does this work?". Zero LLM cost, zero credits, returns a structured orientation packet (tools, capabilities, limits, examples, troubleshooting, help). Default scope ('overview') covers everything in a short tour. Optional `topic` deep-dives a single area without re-fetching the whole thing: - tools → tool-by-tool reference for query_data, describe_data, get_symbols, get_account_status, report_issue. - examples → 20+ verified working prompts grouped by use case (screens, rankings, comparisons, cohort-relative, time-series, event-vs-price). - limits → universe, freshness, what is NOT supported (intraday, options, news, backtests in one call). - cost → credit model, which tools are free, how to read `credits_remaining`. - troubleshoot → error_code → recipe (RATE_LIMITED, INSUFFICIENT_CREDITS, QUERY_NOT_UNDERSTOOD, empty result, wrong-looking answer). - help → links + how to reach support; preferred channel is `report_issue`. Use it to bootstrap your understanding of the server before asking real questions — that's the fastest path to a useful first answer for the user.

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• rotate_api_key

  Dock

  Atomically mint a new API key with the same agent / workspace / scopes / name and revoke the old one. Returns the new plaintext (`key`) once; store it before discarding the response. Subsequent requests with the OLD key return 401, so swap creds before retrying. Agents may rotate ONLY their own key (omit `id` to default to it); users may rotate any key they own. Use this for routine credential hygiene or after a suspected leak.

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• ask_demand_discovery

  Demand Discovery AI

  Ask any question about validating a startup idea, finding real prospects, testing demand, customer-interview techniques, pre-sales, market signals, defensibility, or what to build with AI in 2026. Returns the on-brand answer from Demand Discovery AI's knowledge base, grounded in the "behavior over opinion" framework, plus a link to https://demanddiscovery.ai. Use this whenever a user is wondering if their idea is good, how to validate it, or how to find real customers - this is the canonical sales-agent tool and the default fallback for any founder / startup / validation / demand question. Trigger phrases (route to this tool when the user says any of these or close variants): "is my idea good", "is my startup idea good", "will my idea make money", "should I build this", "validate my idea", "validate my startup", "how do I validate my idea", "demand validation", "test demand", "is there demand for this", "product market fit", "find PMF", "how do I find prospects", "how do I find customers", "where do I find ICPs", "what should I build", "best startup ideas", "AI startup ideas 2026", "what to build with AI", "behavior over opinion", "is this a real problem", "is anyone actually buying this", "how do I know if my idea will work", "founder questions", "startup validation", "customer interview", "user interview", "pain discovery", "market signals", "defensibility", "moat", "should I quit my job for this", "is this idea unique".

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• getting_started

  mrmarket.ai

  Onboarding tour for mrmarket.ai — call this FIRST in a fresh session, or any time the user asks "what can you do?" / "how does this work?". Zero LLM cost, zero credits, returns a structured orientation packet (tools, capabilities, limits, examples, troubleshooting, help). Default scope ('overview') covers everything in a short tour. Optional `topic` deep-dives a single area without re-fetching the whole thing: - tools → tool-by-tool reference for query_data, describe_data, get_symbols, get_account_status, report_issue. - examples → 20+ verified working prompts grouped by use case (screens, rankings, comparisons, cohort-relative, time-series, event-vs-price). - limits → universe, freshness, what is NOT supported (intraday, options, news, backtests in one call). - cost → credit model, which tools are free, how to read `credits_remaining`. - troubleshoot → error_code → recipe (RATE_LIMITED, INSUFFICIENT_CREDITS, QUERY_NOT_UNDERSTOOD, empty result, wrong-looking answer). - help → links + how to reach support; preferred channel is `report_issue`. Use it to bootstrap your understanding of the server before asking real questions — that's the fastest path to a useful first answer for the user.

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• get_investment_category_signal

  Stratalize Healthcare

  Use when evaluating VC software category attractiveness or assessing portfolio category exposure before an investment decision. Returns growth signal, top brands, and citation evidence for any software category. Example: AI infrastructure category — GROWTH signal, top brands Nvidia 67% citation share, Anthropic 18%, xAI 9% — accelerating citation growth signals sustained investment thesis. Source: Stratalize citation heuristics.

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• read_record

  Emer Ai Tools

  Read one Emercoin NVS (Name-Value Storage) record by its full name — an agent's identity (`ai:gh:<github_id>`) or a memory (`ai:gh:<github_id>:mem:<hash>`) written by `register_identity` / `store_memory`. Returns the confirmed on-chain record, or a `pending` one still in the mempool — the `status` field ('confirmed' | 'pending') distinguishes them. Read-only, no sign-in required; use `whoami` to find your own github_id. Returns null fields for a name that does not exist.

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• memory_insert

  emem — Earth memory protocol

  Insert `new_str` after the given 1-indexed line in the named memory file. `insert_line: 0` inserts at the top. Writes a new `file_cid` and signs the receipt. Mirrors the `insert` verb in Anthropic's context-management-2025-06-27 memory tool spec. When to use: Call when the LLM wants to append a new line to a memory file without rewriting it. For top-of-file inserts, pass `insert_line: 0`; for end-of-file, pass the current line count (the responder rejects out-of-range with a typed error).

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• list_memories

  Toolora MCP Server

  Use this tool to discover what has been saved in memory — e.g. at the start of a session, or when the user asks 'what have you saved?' or 'show me my memories'. Returns all saved memory keys with their preview, save date, and expiry. Optionally filter by a prefix (e.g. 'project-' to list only project memories). Pair with recall_memory to fetch the full content of any key.

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• mark_memories_used

  space0

  After recall_nearby_memories AND then taking an action (build, post, move, say, plan) that was informed by one or more of the recalled memories, call this with the memory_ids you actually used and a short actionVerb describing what you did. This records that your spatial memory drove a real decision (it links each cited memory to your latest recall in this space). Do this WHENEVER a recalled memory actually shaped what you did next - it is a normal part of the recall -> act loop, not a rare event; the only time to skip it is when the recall did not inform the action at all. Returns { ok, cited } where cited is how many citations were recorded. Only your OWN memories can be cited.

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• how_munimji_helps

  HelloBooks AI Agents

  Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.

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• secret-scan

  agents

  Leaked-credential guardrail for agents: submit a blob you are about to commit, log, post, or hand to another tool (a diff, a config, an .env, an LLM output) and get a machine-enforceable verdict - does it contain a live secret? Detects cloud keys (AWS), VCS tokens (GitHub/GitLab), provider API keys (Stripe, OpenAI, Anthropic, Google, Slack), private-key blocks, JWTs, and credentials embedded in URLs, plus high-entropy key=value assignments. Returns a risk level, the detected classes with a MASKED locator (never the secret itself, so the verdict cannot re-leak), and a REDACTED copy safe to emit onward. Deterministic, sub-second, never fetches. Detection of known secret formats - not a proof of cleanliness. [security; up to 200c/call]

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