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459,989 tools. Updated 2026-08-17 10:15

"Techniques to Improve Agent Programming" matching MCP tools:

  • Corrects the category of one or more transactions (PATCH /transactions/:id). Pass `items` as an array of { transaction_id, category_id } — `transaction_id` comes from openfinance_list_transactions, `category_id` from openfinance_list_categories. This overrides Pluggy's automatic categorization AND teaches Pluggy: recategorizing a transaction automatically creates a Category Rule for this client (case-insensitive exact match on the transaction's data), so FUTURE similar transactions are categorized the same way — use this to fix miscategorized transactions and improve categorization accuracy going forward. Batch shape: returns `{ updated, results: [{ transaction_id, category, categoryId }], errors: [{ id, status, message }] }` — per-item errors do not fail the whole batch.
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  • Search the MITRE D3FEND catalog of defensive techniques by keyword, tactic, or targeted artifact. Default response is SLIM (drops `uri` from each row — saves ~60 chars/row, ~30% on popular drills); pass include='full' for the verbose record. Pass exclude_id when chaining from d3fend_defense_lookup to skip self in sibling-artifact searches. Use to discover defenses applicable to a given threat model — e.g. 'what defenses harden access tokens?' (tactic=Harden + artifact='Access Token'). Drill into d3fend_defense_lookup with any returned defense_id for the ATT&CK technique mappings. Free: 30/hr, Pro: 500/hr. Returns {query, total, results [{defense_id, label, uri (only when include=full), parent_label, tactic, artifact}], next_calls}.
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  • Return canonical synthesis / patching techniques with role-keyed module realizations drawn from the corpus. Use this when the user asks "how do I do X?" with X being a recognisable technique (low-pass-gate plucks, pinged-filter percussion, parallel multiband processing, complex-oscillator FM, karplus-strong pluck, clocked-delay feedback, modal-resonator excitation, wavefolder harmonics, envelope-follower ducking, Maths-style function-generator omnibus). It's also the right tool when the user has a module and asks "what's this good for?" — pass filter.module_id to retrieve every technique that references the module via its role_realizations. Each technique declares role_definitions (the roles the technique uses, each with required and optional affordances) and role_realizations (concrete modules that fill each role, with the affordances they provide). The model substitutes modules from the user's rack into roles by affordance match — DO NOT treat the realization list as exhaustive or as a recipe. Args: - filter (optional): { capability?, module_id?, text? } - capability: kebab-case capability id (see search_modules _meta.taxonomy). Returns techniques whose required *or* optional capability list includes this id. - module_id: "<manufacturer>/<module-slug>". Returns techniques that have a role_realization referencing this module. - text: free-text phrase. Substring-matches against technique id/label/description AND a curated alias table (technique_aliases) — that's the right surface when a user types evocative prose like "stuttering delay", "plucked string", "source of uncertainty" that doesn't grep against any kebab-case id. Two-way alias match: long alias ("source of uncertainty") matches short query ("uncertainty"), and vice versa. - When multiple filters supplied, AND-intersects. - Omit filter entirely to list all techniques. Returns: { "techniques": [ { "id": "low-pass-gate-pluck", "label": "Low-Pass Gate Pluck", "description": "Send a short envelope...", "required_capabilities": ["lowpass-gate"], "optional_capabilities": ["envelope-generator", "function-generator"], "role_definitions": [ { "role_id": "lpg", "description": "The vactrol-based or vactrol-emulating element. Strictly required...", "required_affordances": ["lowpass-gate"], "optional_affordances": [] }, ... ], "role_realizations": [ { "role_id": "lpg", "module_id": "make-noise/optomix", "affordances_provided": ["lowpass-gate"], "notes": "Two-channel vactrol-based LPG..." }, ... ], "canonical_instance": { "rationale": "...", "lineage": [ { "position": 1, "label": "Buchla 292 (1970)", "module_id": null, "notes": "..." }, { "position": 2, "label": "Tiptop Audio Buchla 292t", "module_id": "tiptop-audio/buchla-292t" }, ... ] }, "counter_canonical_notes": [ { "claim_pushed_back_against": "Optomix is the canonical pairing with Plaits...", "evidence": "The corpus catalogs 19 LPG-capable modules..." } ], "coverage": [ { "role_id": "voice", "realizations_count": 3 }, { "role_id": "lpg", "realizations_count": 19 }, { "role_id": "env", "realizations_count": 6 }, { "role_id": "clock", "realizations_count": 2 } ] } ], "_meta": { "filter": {...}, "feedback_hint"?: string } } How to use role data: - role_realizations are CURATORIAL SAMPLES, not exhaustive lists. The coverage[].realizations_count tells you how many are documented; other modules may fill the same role. - To find modules in the user's rack that can fill a role, use find_role_realizations(technique_id, role_id, available_modules). - canonical_instance is opt-in and sparse. Most techniques don't have one; that absence is information. When present, it documents a documented historical lineage (e.g., Buchla 292 → 292t → MMG → Optomix for low-pass-gate-pluck) — NOT a prescription. - counter_canonical_notes push back on likely training-data priors. When the user invokes a canonical-sounding claim that has a counter_canonical_note, surface the pushback. Errors: - "Module not found: <id>" if filter.module_id is supplied and unknown. - Empty techniques[] with a feedback_hint when filters produce no matches — call report_gap if the user expected coverage.
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  • Send structured feedback to the Kifly team. **Call after a confusing response, a dead-end, or a successful workaround you had to invent** — it's how we improve the agent surface. Fire-and-forget: returns 202 immediately, no blocking, safe to skip if it would add latency to a user-facing flow. `category` and `severity` are required enums (don't free-form them). Include `context` with what you were doing (tool called, query used, response shape, what you expected). Add `suggested_fix` only if you have a concrete idea. Rate-limited to 10/min per agent token; everything is reviewed before influencing anything.
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  • Corrects the category of one or more transactions (PATCH /transactions/:id). Pass `items` as an array of { transaction_id, category_id } — `transaction_id` comes from openfinance_list_transactions, `category_id` from openfinance_list_categories. This overrides Pluggy's automatic categorization AND teaches Pluggy: recategorizing a transaction automatically creates a Category Rule for this client (case-insensitive exact match on the transaction's data), so FUTURE similar transactions are categorized the same way — use this to fix miscategorized transactions and improve categorization accuracy going forward. Batch shape: returns `{ updated, results: [{ transaction_id, category, categoryId }], errors: [{ id, status, message }] }` — per-item errors do not fail the whole batch.
    Connector
  • Search the MITRE ATLAS catalog of AI/ML attack techniques by keyword, tactic, or maturity. Default response is SLIM (description truncated to 240 chars per row); pass include='full' for the verbose record. Pass exclude_id when chaining from atlas_technique_lookup to skip self in sibling-tactic searches. Use this to discover techniques matching a threat-model question, e.g. 'what techniques target LLM serving infrastructure?'. Drill into atlas_technique_lookup with any returned technique_id for the full description, ATT&CK bridge, and pivot hints. For broader cross-referencing: when a result has attack_reference_id, that bridges to D3FEND mitigations via d3fend_defense_for_attack. Free: 30/hr, Pro: 500/hr. Returns {query (echoed filters), total, results [{technique_id, name, description (truncated by default), tactics, inherited_tactics, maturity, attack_reference_id, subtechnique_of}], next_calls}.
    Connector

Matching MCP Servers

Matching MCP Connectors

  • Connect any two AI agents and let them talk directly. Claude to Claude, Claude to OpenAI, or any MCP-compatible agent

  • Transform any blog post or article URL into ready-to-post social media content for Twitter/X threads, LinkedIn posts, Instagram captions, Facebook posts, and email newsletters. Pay-per-event: $0.07 for all 5 platforms, $0.03 for single platform.

  • Look up a MITRE ATLAS case study — a documented real-world AI/ML attack incident. Each case study links a sequence of ATLAS techniques (techniques_used) to the incident. Default response is SLIM (description truncated to 240 chars); pass include='full' for the verbose narrative. Use this after atlas_technique_search to find which incidents have exercised a given technique. Drill into the full techniques_used array via bulk_atlas_technique_lookup in a single call (next_calls emits exactly that hint). Returns 404 when the id is not in the synced catalog. Free: 30/hr, Pro: 500/hr. Returns {case_study_id, name, description, techniques_used, next_calls}.
    Connector
  • Rewrite a prompt to score higher on the PQS rubric, AND show before/after output comparisons so the user can see the impact. Returns the optimized prompt, the original PQS score, the optimized PQS score, and side-by-side sample outputs from a frontier model using both versions. USE WHEN: - The user got a low score from score_prompt and asks how to improve. - The user explicitly asks to "improve" / "rewrite" / "fix" / "optimize" a prompt they pasted. - The user is dissatisfied with output quality from a previous prompt and asks how to get better results. - score_prompt returned a suggestion to invoke this tool. DO NOT USE WHEN: - The user just asked for a score (use score_prompt only — don't double up). - The user wants you to write a new prompt from scratch (write it directly). REQUIRES: A PQS API key from a Pro subscription ($19.99/month, 1,000 calls/mo, includes batch + A/B comparison). If the user has not provided one, the tool returns a clear subscription URL — pass that response to the user verbatim. Do not invent or guess API keys. There is no free trial of this tool; the user must subscribe before the first call. COST: Counted against your Pro subscription's monthly call quota. LATENCY: ~6-8 seconds.
    Connector
  • Rewrite a prompt to score higher on the PQS rubric, AND show before/after output comparisons so the user can see the impact. Returns the optimized prompt, the original PQS score, the optimized PQS score, and side-by-side sample outputs from a frontier model using both versions. USE WHEN: - The user got a low score from score_prompt and asks how to improve. - The user explicitly asks to "improve" / "rewrite" / "fix" / "optimize" a prompt they pasted. - The user is dissatisfied with output quality from a previous prompt and asks how to get better results. - score_prompt returned a suggestion to invoke this tool. DO NOT USE WHEN: - The user just asked for a score (use score_prompt only — don't double up). - The user wants you to write a new prompt from scratch (write it directly). REQUIRES: A PQS API key from a Pro subscription ($19.99/month, 1,000 calls/mo, includes batch + A/B comparison). If the user has not provided one, the tool returns a clear subscription URL — pass that response to the user verbatim. Do not invent or guess API keys. There is no free trial of this tool; the user must subscribe before the first call. COST: Counted against your Pro subscription's monthly call quota. LATENCY: ~6-8 seconds.
    Connector
  • Corrects the category of one or more transactions (PATCH /transactions/:id). Pass `items` as an array of { transaction_id, category_id } — `transaction_id` comes from openfinance_list_transactions, `category_id` from openfinance_list_categories. This overrides Pluggy's automatic categorization AND teaches Pluggy: recategorizing a transaction automatically creates a Category Rule for this client (case-insensitive exact match on the transaction's data), so FUTURE similar transactions are categorized the same way — use this to fix miscategorized transactions and improve categorization accuracy going forward. Batch shape: returns `{ updated, results: [{ transaction_id, category, categoryId }], errors: [{ id, status, message }] }` — per-item errors do not fail the whole batch.
    Connector
  • Corrects the category of one or more transactions (PATCH /transactions/:id). Pass `items` as an array of { transaction_id, category_id } — `transaction_id` comes from openfinance_list_transactions, `category_id` from openfinance_list_categories. This overrides Pluggy's automatic categorization AND teaches Pluggy: recategorizing a transaction automatically creates a Category Rule for this client (case-insensitive exact match on the transaction's data), so FUTURE similar transactions are categorized the same way — use this to fix miscategorized transactions and improve categorization accuracy going forward. Batch shape: returns `{ updated, results: [{ transaction_id, category, categoryId }], errors: [{ id, status, message }] }` — per-item errors do not fail the whole batch.
    Connector
  • Corrects the category of one or more transactions (PATCH /transactions/:id). Pass `items` as an array of { transaction_id, category_id } — `transaction_id` comes from openfinance_list_transactions, `category_id` from openfinance_list_categories. This overrides Pluggy's automatic categorization AND teaches Pluggy: recategorizing a transaction automatically creates a Category Rule for this client (case-insensitive exact match on the transaction's data), so FUTURE similar transactions are categorized the same way — use this to fix miscategorized transactions and improve categorization accuracy going forward. Batch shape: returns `{ updated, results: [{ transaction_id, category, categoryId }], errors: [{ id, status, message }] }` — per-item errors do not fail the whole batch.
    Connector
  • Corrects the category of one or more transactions (PATCH /transactions/:id). Pass `items` as an array of { transaction_id, category_id } — `transaction_id` comes from openfinance_list_transactions, `category_id` from openfinance_list_categories. This overrides Pluggy's automatic categorization AND teaches Pluggy: recategorizing a transaction automatically creates a Category Rule for this client (case-insensitive exact match on the transaction's data), so FUTURE similar transactions are categorized the same way — use this to fix miscategorized transactions and improve categorization accuracy going forward. Batch shape: returns `{ updated, results: [{ transaction_id, category, categoryId }], errors: [{ id, status, message }] }` — per-item errors do not fail the whole batch.
    Connector
  • Send structured feedback to the Kifly team. **Call after a confusing response, a dead-end, or a successful workaround you had to invent** — it's how we improve the agent surface. Fire-and-forget: returns 202 immediately, no blocking, safe to skip if it would add latency to a user-facing flow. `category` and `severity` are required enums (don't free-form them). Include `context` with what you were doing (tool called, query used, response shape, what you expected). Add `suggested_fix` only if you have a concrete idea. Rate-limited to 10/min per agent token; everything is reviewed before influencing anything.
    Connector
  • Corrects the category of one or more transactions (PATCH /transactions/:id). Pass `items` as an array of { transaction_id, category_id } — `transaction_id` comes from openfinance_list_transactions, `category_id` from openfinance_list_categories. This overrides Pluggy's automatic categorization AND teaches Pluggy: recategorizing a transaction automatically creates a Category Rule for this client (case-insensitive exact match on the transaction's data), so FUTURE similar transactions are categorized the same way — use this to fix miscategorized transactions and improve categorization accuracy going forward. Batch shape: returns `{ updated, results: [{ transaction_id, category, categoryId }], errors: [{ id, status, message }] }` — per-item errors do not fail the whole batch.
    Connector
  • Corrects the category of one or more transactions (PATCH /transactions/:id). Pass `items` as an array of { transaction_id, category_id } — `transaction_id` comes from openfinance_list_transactions, `category_id` from openfinance_list_categories. This overrides Pluggy's automatic categorization AND teaches Pluggy: recategorizing a transaction automatically creates a Category Rule for this client (case-insensitive exact match on the transaction's data), so FUTURE similar transactions are categorized the same way — use this to fix miscategorized transactions and improve categorization accuracy going forward. Batch shape: returns `{ updated, results: [{ transaction_id, category, categoryId }], errors: [{ id, status, message }] }` — per-item errors do not fail the whole batch.
    Connector
  • Corrects the category of one or more transactions (PATCH /transactions/:id). Pass `items` as an array of { transaction_id, category_id } — `transaction_id` comes from openfinance_list_transactions, `category_id` from openfinance_list_categories. This overrides Pluggy's automatic categorization AND teaches Pluggy: recategorizing a transaction automatically creates a Category Rule for this client (case-insensitive exact match on the transaction's data), so FUTURE similar transactions are categorized the same way — use this to fix miscategorized transactions and improve categorization accuracy going forward. Batch shape: returns `{ updated, results: [{ transaction_id, category, categoryId }], errors: [{ id, status, message }] }` — per-item errors do not fail the whole batch.
    Connector
  • Corrects the category of one or more transactions (PATCH /transactions/:id). Pass `items` as an array of { transaction_id, category_id } — `transaction_id` comes from openfinance_list_transactions, `category_id` from openfinance_list_categories. This overrides Pluggy's automatic categorization AND teaches Pluggy: recategorizing a transaction automatically creates a Category Rule for this client (case-insensitive exact match on the transaction's data), so FUTURE similar transactions are categorized the same way — use this to fix miscategorized transactions and improve categorization accuracy going forward. Batch shape: returns `{ updated, results: [{ transaction_id, category, categoryId }], errors: [{ id, status, message }] }` — per-item errors do not fail the whole batch.
    Connector
  • Corrects the category of one or more transactions (PATCH /transactions/:id). Pass `items` as an array of { transaction_id, category_id } — `transaction_id` comes from openfinance_list_transactions, `category_id` from openfinance_list_categories. This overrides Pluggy's automatic categorization AND teaches Pluggy: recategorizing a transaction automatically creates a Category Rule for this client (case-insensitive exact match on the transaction's data), so FUTURE similar transactions are categorized the same way — use this to fix miscategorized transactions and improve categorization accuracy going forward. Batch shape: returns `{ updated, results: [{ transaction_id, category, categoryId }], errors: [{ id, status, message }] }` — per-item errors do not fail the whole batch.
    Connector
  • Corrects the category of one or more transactions (PATCH /transactions/:id). Pass `items` as an array of { transaction_id, category_id } — `transaction_id` comes from openfinance_list_transactions, `category_id` from openfinance_list_categories. This overrides Pluggy's automatic categorization AND teaches Pluggy: recategorizing a transaction automatically creates a Category Rule for this client (case-insensitive exact match on the transaction's data), so FUTURE similar transactions are categorized the same way — use this to fix miscategorized transactions and improve categorization accuracy going forward. Batch shape: returns `{ updated, results: [{ transaction_id, category, categoryId }], errors: [{ id, status, message }] }` — per-item errors do not fail the whole batch.
    Connector