keel
Keel
Die MCP-Steuerungsebene, die Scanner-Rauschen in Jäger-taugliche, nicht-destruktive Beweise verwandelt
Dreizehn MCP-Tools. Semantische Deduplizierung. Ratenlimits pro Host. Sichere Beweise, die zeigen, was ein Jäger tun kann – ohne das Ziel zu beschädigen.
Warum Keel · Installation · Clients · Beweise · Werkzeuge
150 Tools auf einen Agenten zu werfen ist einfach. Die schwierigen Probleme sind Deduplizierung über Tools hinweg, ausnutzbar vs. Rauschen und das Ziel nicht zu überlasten. Keel ist die Steuerungsebene für diese drei.
Ein KI-Client spricht mit Keel, nicht mit httpx, nuclei oder einer Shell. Keel entwirft jeweils eine Welle, setzt Scope- und Ratenlimits durch, führt Scanner-Treffer zu semantischen Karten zusammen und führt GET-only-Playbooks auf testereigenen Daten aus. Wenn ein Playbook proven zurückgibt, erhalten Sie einen curl-Replay, dem ein Jäger folgen kann – weiterhin ohne Schreibzugriffe, Shells oder Payload-Spam.
Verwenden Sie es nur für Programme, die Sie testen dürfen.
Warum Keel
Schwieriges Problem | Was Scanner-Dumps tun | Was Keel tut |
Deduplizierung über Tools | Eine Nuclei-Template-ID pro Zeile; dieselbe IDOR erscheint fünfmal | Semantischer Schlüssel aus Schwachstellenklasse + normalisierter Route + Methode + Parameter. UUID-/ID-/Hex-Tokens kollabieren. Kompatible Beobachtungen werden zusammengeführt. |
Ausnutzbar vs. Rauschen | Hoher Schweregrad = "raus damit" | Karten durchlaufen |
Das Ziel nicht überlasten | Alle Templates gleichzeitig abfeuern, bei 429 erneut versuchen | Eine aktive Welle pro Host, Token-Buckets, Nuclei-Concurrency 1, kein OAST, keine Redirects, keine unsignierten Templates, keine dos/fuzz/bruteforce/intrusive-Tags. HTTP 429 wird zu einer Abkühlphase. |
Ein Wrapper, der eine riesige Toolbox per Shell aufruft, hat diese Ebene nicht. Keel hat sie – im Scheduler, in den Adaptern und im Proof-Broker.
Related MCP server: BountyProof MCP
Architektur
flowchart TD
A[AI coding client] -->|stdio MCP| B[Keel]
B --> C[Scope and rate gate]
C --> W[Background job and wave scheduler]
W --> H[httpx: one target]
W --> N[nuclei: HTTP templates, bounded]
C --> P[Proof broker: GET only]
P --> T[Tester-owned resource]
H --> S[Semantic card store]
N --> S
P --> S
S --> Q[Triage and evidence states]begin_engagementmit dem Hostnamen, den Sie testen dürfen.draft_wavesschlägt Erreichbarkeit plus Template-Mikrowellen vor. Noch kein Traffic.execute_wavegibt sofort einen Job zurück. Pollen Siewave_status.cancel_wavebeendet den Scanner.query_cardsliefert Jäger-relevante Karten.assess_exploitabilitysagt, was es beweisen würde.draft_proofund dannexecute_proofführen ein GET-only-Playbook gegen Testerdaten aus.provenbedeutet, dass die Invariante gehalten hat.protectedbedeutet, dass die Kontrolle funktioniert hat.
Installation
macOS (Homebrew). pipx ist ein separates Tool – installieren Sie es zuerst. Apples /usr/bin/python3 ist oft 3.9 und kann Keel nicht installieren.
brew install pipx python@3.12
pipx ensurepath
# open a new terminal, then:
pipx install keel-pentest
keel-pentest setup
keel-pentest doctorWenn python3.12 bereits auf dem Rechner ist und Sie kein Homebrew-pipx möchten:
python3.12 -m pip install --user pipx
python3.12 -m pipx ensurepath
python3.12 -m pipx install keel-pentestsetup lädt ProjectDiscovery httpx und nuclei in ~/.keel/bin herunter. Keel findet sie dort, auch wenn ein GUI-Client einen dünnen PATH hat. Kein zusätzliches KEEL_HTTPX_BIN für den ersten Scan.
Richten Sie dann Ihren MCP-Client auf die ausführbare Datei keel-pentest aus:
claude mcp add --scope user --transport stdio keel -- keel-pentest
codex mcp add keel -- keel-pentest
hermes mcp add keel --command keel-pentestOpenCode: "command": ["keel-pentest"].
Python 3.10+. Führen Sie nicht pip install keel aus – das ist ein anderes Projekt. OS-Hinweise und pip/venv: INSTALL.md. Client-Formen: clients/README.md.
Optional später: KEEL_APPROVAL_FILE für ein Team-Manifest, das Scope, Template-IDs und Proof-Ziele festlegt. Der Standardmodus ist selbst attestiert – begin_engagement ist die Autorisierung. Ratenlimits, eine Welle pro Host, signierte Templates und bereinigte Beweise gelten weiterhin.
Sichere Beweise, die trotzdem Impact belegen
Scanner-Ausgabe ist eine Hypothese. Keel beweist (oder widerlegt) sie mit Wegwerf-Testerkonten und einem eindeutigen Canary. Jedes Playbook ist GET-only, budgetiert und liefert einen curl-Replay. Der Replay ist das Berichtsartefakt: Wenn das nicht behoben ist, kann ein Jäger mit einem normalen Konto das tun.
Playbook | Beweist | Wie, ohne Schaden |
| IDOR / BOLA | Tester A liest seinen Canary; Tester B führt einen GET auf dieselbe A-eigene URL aus. Identischer Canary + 2xx = |
| Reflektiertes XSS / HTML-Injection | Ein eindeutiger Marker plus eine harmlose |
| Offene Weiterleitung | Richten Sie den Redirect-Parameter auf |
| Fehlende AuthZ | Die Baseline von Tester A muss den Canary zeigen; dieselbe URL ohne Anmeldedaten darf es nicht. 2xx + Canary ohne Authentifizierung = |
| Nur Erreichbarkeit | A liest seinen eigenen Canary. Das ist |
execute_proof speichert Statuscodes, Canary-Booleans, Kürzungsflags, Hashes, Jäger-Impact-Text und das Repro-Skript. Es speichert keine Antwortkörper oder Geheimnisse.
Platzieren Sie einen nicht-geheimen Canary in einem testereigenen Objekt vor cross_account_read / unauth_access_probe. Reflektiertes XSS und offene Weiterleitung injizieren den Marker selbst.
MCP-Werkzeuge
Tool | Rolle |
| Scope und Traffic-Obergrenzen registrieren |
| Erreichbarkeit + Template-Mikrowellen vorschlagen; kein Traffic |
| Einen Hintergrundjob in die Warteschlange stellen |
| Phase, Fortschritt, Ergebnis; |
| Einen gequeueten oder laufenden Scanner stoppen |
| Priorisierte semantische Karten |
| Nur das ursprüngliche Nuclei-Template erneut ausführen |
| Kandidaten-Impact, fehlende Beweise, Negativkontrolle, Playbooks |
| Eine Jäger-Hypothese aufzeichnen |
| Allowlist-Proof-Plan; kein Traffic |
| Das GET-only-Playbook ausführen |
| Abkühlphasen, Budgets, anstehende Wellen |
| Nur-Anhängen-Anwendungsereignisse |
begin_engagement benötigt engagement_id und scope_hosts (einfache Hostnamen, z. B. target.example). Standardwerte: 3 req/s, ein Host gleichzeitig, 120s / 120 Anfragen pro Welle. allow_safe_proof=true aktiviert Beweise. Übergeben Sie nur Namen von Tester-Anmeldedaten; legen Sie Geheimnisse in KEEL_CREDENTIALS_FILE ab.
Beispiel-Prompt
Use only Keel MCP tools. Do not shell out to httpx, nuclei, curl, or exploit tools.
1. begin_engagement for bb-2026-01 with scope_hosts ["target.example"], 3 req/s.
Set allow_safe_proof true if I will run proofs.
2. draft_waves for https://target.example.
3. execute_wave for each wave. Poll wave_status until completed, retryable_failed,
terminal_failed, or cancelled.
4. query_cards (include_noise false), then assess_exploitability on candidates.
5. For a card with a safe playbook, draft_proof then execute_proof using tester
credential names and the canary I planted. Treat protected as refuted.
6. Summarize duplicates, evidence state, hunter_impact, and the repro_script.
Claim exploitable only when Keel reports proven.Traffic-Kontrollen
Exakter Scope und Ausschlüsse bei Entwurf, Zulassung, Aufnahme und Beweis
Eine Welle pro Host; Jobs für denselben Host warten
Gemeinsame globale und pro-Host-Token-Buckets
Persistente Anfrage-Reservierungen; Wiederholungen verbrauchen eine neue Reservierung
Nuclei: signierte HTTP-Templates, kein OAST, keine Redirects, keine Wiederholungen, dos/fuzz/bruteforce/intrusive ausschließen
Isolierte leere Scanner-Konfigurationen; Proxy- und ProjectDiscovery-Cloud-Umgebungsvariablen werden entfernt
HTTP 429 stoppt die Welle und respektiert Retry-After
Begrenzte Antwort-Lesevorgänge; Beweise ohne rohe Körper
Fehlerbehebung
keel-pentest doctor
keel-pentest setup # if doctor reports missing httpx/nucleibegin_engagement nach einem Client-Neustart stellt die SQLite-Engagement wieder her. Wenn Sie den Scope geändert haben, verwenden Sie eine neue engagement_id.
Beweise benötigen allow_safe_proof=true und für Session-Playbooks KEEL_CREDENTIALS_FILE, das Namen wie tester-a auf Authorization oder Cookie abbildet.
Lizenz
MIT. Copyright (c) 2026 Lutfi Z.P.
PyPI: keel-pentest. MCP Registry: io.github.lutfizp/keel. Quellcode: github.com/lutfizp/keel.
Available Tools
9 toolsbegin_engagementC
Register scope, rate limits, and proof flags for one engagement.
| Name | Required | Description | Default |
|---|---|---|---|
| scope_hosts | Yes | ||
| engagement_id | Yes | ||
| exclude_hosts | No | ||
| allow_safe_proof | No | ||
| tester_account_a | No | ||
| tester_account_b | No | ||
| operator_confirmed | No | ||
| requests_per_second | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description does not disclose any side effects, permissions, or error behaviors. Without annotations, the description is insufficient to understand what happens when the tool is invoked.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence without unnecessary words. It is well-structured and easy to read.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool lacks an output schema and annotations, and the description does not mention what the response contains, possible errors, or any other context. It is insufficient for an agent to understand the full behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions 'scope,' 'rate limits,' and 'proof flags' which partially map to parameters like scope_hosts and requests_per_second, but it does not explain the meaning or format of each parameter. The schema has no parameter descriptions, so the description does not compensate adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Register') and the resource ('one engagement'), distinguishing it from siblings that focus on proof execution or health checks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool over alternatives. It does not mention any preconditions or scenarios where this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
draft_proofD
Describe an allowlisted proof without sending traffic.
| Name | Required | Description | Default |
|---|---|---|---|
| card_id | Yes | ||
| playbook_id | Yes | ||
| engagement_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It hints at being non-destructive by saying 'without sending traffic,' but does not explain what drafting entails, side effects, or response behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is succinct but too sparse to be effective. It lacks necessary detail while also not being well-structured to convey core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, no parameter descriptions, and a vague purpose, the agent has insufficient information to determine when or how to invoke this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters (engagement_id, card_id, playbook_id) have no descriptions in the schema or prose. Coverage is 0%, and the description adds no meaning beyond the parameter names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Describe an allowlisted proof' is vague; 'describe' is not a strong verb for the action, and 'allowlisted proof' is ambiguous. It does not clearly distinguish itself from sibling tools like execute_proof or draft_waves.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The only usage hint is a negative constraint ('without sending traffic'), which is insufficient. No positive conditions or comparisons to alternatives (e.g., when to use draft_proof vs execute_proof) are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
draft_wavesA
Propose probe_alive then template_scan waves without executing them.
| Name | Required | Description | Default |
|---|---|---|---|
| seed_url | Yes | ||
| engagement_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It usefully states that the tool does not execute the waves and specifies the wave order. However, it does not disclose whether the proposal persists, requires permissions, or has any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short, front-loaded sentence with no filler. Every word contributes meaning, and the core distinction ('without executing them') is stated directly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with only two required scalar parameters and no output schema, so the description does not need much. It covers the main purpose and non-execution, but it omits what the proposal produces or returns and how the parameters relate to the waves.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides only names and types with no descriptions, and the description never mentions the parameters. 'seed_url' and 'engagement_id' are somewhat self-explanatory, but the 0% schema coverage is not compensated by any parameter-level guidance in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('propose'), identifies the resource ('waves'), and names the exact wave sequence ('probe_alive then template_scan'). The phrase 'without executing them' clearly distinguishes this tool from the sibling execute_wave.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when you want to preview or plan waves before execution, and 'without executing them' effectively rules out execute_wave. It does not explicitly name alternatives or state when to switch to execution, but the intended context is reasonably clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
engagement_healthC
Report registered engagements, cooldowns, and pending waves.
| Name | Required | Description | Default |
|---|---|---|---|
| engagement_id | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Report' suggests a read-only style operation, but the description does not explicitly state that no state changes occur, does not mention auth requirements or side effects, and provides no detail about what 'registered' or 'pending' statuses mean. With no output schema, return behavior is also undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single compact sentence with no filler or repetition. It front-loads the verb and the key reported categories, which makes it easy to scan, although it is terse enough that it contributes to under-specification in other dimensions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and no parameter descriptions, this one-line description is not enough for a fully informed call. A no-argument health check is guessable, but the behavior of engagement_id, the meaning of 'registered,' and the relationship to sibling status tools like query_cards and state_impact are left unexplained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description never mentions engagement_id. The agent cannot tell whether the optional parameter filters the report to one engagement, scopes the results, or is required for a valid call. The description adds no meaning beyond the raw property name in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Report' and names concrete resources: registered engagements, cooldowns, and pending waves. This makes the tool's core purpose clear and distinguishes it from the execution-focused siblings like execute_wave and begin_engagement, though it does not explicitly differentiate it from query-oriented siblings like query_cards.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. The description states what it reports but does not mention prerequisites, exclusions, or a preferred context such as 'check status before executing a wave.' An agent would have to infer usage from the tool name and sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
execute_proofC
Run an allowlisted proof. Requires allow_safe_proof and operator_confirmed.
| Name | Required | Description | Default |
|---|---|---|---|
| card_id | Yes | ||
| session_a | Yes | ||
| session_b | No | ||
| playbook_id | Yes | ||
| engagement_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears the full burden of behavioral disclosure, and it does reveal a meaningful precondition — an allowlisted proof and operator confirmation — implying an approval gate beyond the schema's surface, which is useful. However, it says nothing about side effects, return values, reversibility, or whether execution is long-running, a notable gap for an 'execute' tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler and the preconditions are stated directly. It is concise to the point of thinness — the efficiency is real, but the brevity reflects under-specification rather than disciplined economy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero annotations, no output schema, and 0% schema description coverage, the description leaves critical information uncovered: the meaning of a 'proof', expected parameter values, and the outcome of execution. For a 5-parameter (4 required) tool, this is incomplete and would leave an agent uncertain how to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description offers no compensatory explanation of card_id, session_a, session_b, playbook_id, or engagement_id, or how they interrelate. With five unannotated string parameters, the agent is left guessing at values and formats, which the description failed to address.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('run') and a resource ('allowlisted proof'), with the 'allowlisted' qualifier adding an authorization constraint that helps set context. However, it never defines what a 'proof' is or differentiates this from close siblings like execute_wave and draft_proof, leaving the agent to infer the distinction on its own.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to choose this tool over its siblings, despite obvious ambiguity with execute_wave, draft_proof, and draft_waves. The 'Requires allow_safe_proof and operator_confirmed' line reads as a precondition rather than a usage context, and no alternatives or exclusions are named.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
execute_waveC
Run one admitted wave behind the per-host token bucket.
| Name | Required | Description | Default |
|---|---|---|---|
| wave_id | Yes | ||
| engagement_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing side effects. 'Run one admitted wave' hints at a mutating action but does not state whether it is idempotent, what happens to the wave, what errors occur, or what the rate limit entails. The token bucket reference suggests throttling but lacks concrete behavioral details expected for an execute-style operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, dense sentence that front-loads the core action with no filler words. Every word contributes to the intended meaning, and it is brief. However, its extreme brevity sacrifices clarity—conciseness is not a substitute for explaining 'admitted' or the token bucket without further context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Even though the tool is simple (2 string params, no output schema), the description fails to cover key aspects like return values, side effects, or the meaning of 'admitted' and 'per-host token bucket.' For a mutating tool with no annotations, more behavioral context is necessary. The lack of any output or error information makes it incomplete for an agent to call this safely.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not mention either 'wave_id' or 'engagement_id'. There is no explanation of how the parameters influence execution or what 'admitted' means for them. The description provides zero help in understanding parameter semantics, leaving the agent completely reliant on parameter names alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description provides a verb ('run') and a resource ('wave') with additional context about a token bucket, but the meaning of 'admitted wave' is jargon-heavy and unclear without domain knowledge. It does not clearly differentiate from the sibling 'execute_proof'—both suggest executing something. It is not a tautology, but it fails to concretely state what the tool does or what a 'wave' is.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus the siblings. It does not mention alternatives like 'execute_proof' or conditions under which a wave is 'admitted.' The token bucket hint implies rate limiting but does not explain when a user should call this versus other execution tools. No exclusions or prerequisites are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_cardsB
Return hunter-relevant cards. Informational and hardening are hidden by default.
| Name | Required | Description | Default |
|---|---|---|---|
| engagement_id | Yes | ||
| include_noise | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the full burden of behavioral disclosure. It does state a key behavior: informational and hardening cards are hidden by default, which tells the agent about default filtering. However, it does not mention whether the tool is read-only, any permission requirements, rate limits, or failure modes. The non-mutating nature of a 'query' is implied but not explicitly stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely compact at two short sentences, leading with the primary purpose. It avoids redundancy and wastes no words, though it could have used the available space to clarify parameters or usage since it is so brief.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema and no annotations, the description is incomplete. It does not describe the return format, possible results, pagination, error cases, or what constitutes 'hunter-relevant'. The single behavioral note about default hiding is helpful but does not make the tool safely callable by an agent that needs to know what to expect or how to interpret the output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must explain the parameters. It does not explain what engagement_id is or what include_noise does beyond default false. The text 'Informational and hardening are hidden by default' indirectly suggests include_noise might control showing those, but it never explicitly links the parameter to that behavior. The agent is left guessing about the meaning and usage of both parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool returns 'hunter-relevant cards', which is a clear verb (return/query) and resource (cards). It does not formally distinguish itself from sibling tools, but the action-oriented siblings (execute_proof, draft_waves, etc.) are clearly different, so the purpose is recognizable without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this is a query tool for retrieving cards, but it gives no explicit guidance on when to use it versus alternatives. The note 'Informational and hardening are hidden by default' hints at the include_noise parameter, but it does not explicitly say 'use include_noise when you need these types of cards' or provide any exclusions relative to other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
second_lookC
Re-run a bounded template scan on a single card URL.
| Name | Required | Description | Default |
|---|---|---|---|
| card_id | Yes | ||
| engagement_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry all behavioral information. It only says 're-run' and 'bounded template scan,' which hints at a read-only operation but does not disclose side effects, auth requirements, rate limits, or return behavior. This is sparse coverage that leaves significant behavioral uncertainty.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, tight sentence with no filler. It is front-loaded with the core action ('re-run') and scope ('bounded template scan'). While it is not verbose, its brevity comes at the cost of missing crucial details, so it earns a 4 for clarity of structure but not a 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of annotations, output schema, and parameter descriptions, the description is severely under-informed. It does not explain what an 'engagement' or 'card' is, what a 'template scan' yields, or how to interpret results. For a tool with two required parameters and no output schema, this is insufficient for an agent to call it correctly without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% (parameters have no descriptions), and the description does not compensate. It mentions 'single card URL,' implying card_id is a URL, but leaves engagement_id unexplained. The agent must infer parameter purpose and types from names alone, which is insufficient.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('re-run'), resource ('bounded template scan'), and object ('single card URL'), making the tool's core function clear. It distinguishes implicitly from siblings like execute_proof or execute_wave by emphasizing a 'second look' on a single card, but it does not explicitly name alternatives, so it falls short of full differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 're-run' implies a use case where a previous scan already occurred and a refresh is needed, offering some contextual guidance. However, there is no explicit mention of when to choose this tool over siblings, and no exclusions are stated. The guidance is present but implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
state_impactC
Record hunter impact_class and preconditions on a card.
| Name | Required | Description | Default |
|---|---|---|---|
| impact | Yes | ||
| card_id | Yes | ||
| hunter_why | Yes | ||
| engagement_id | Yes | ||
| preconditions | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that this is a write operation ('Record'), but with no annotations and no output schema, that's all it reveals. It doesn't specify whether this creates a new record, updates an existing state, requires any authentication, or what happens on repeated calls. For a mutation tool, this is a substantial gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, about eight words, with no filler. It leads with the action and object, making it easy to parse and free of redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With five required parameters, no annotations, and no output schema, this description is too minimal to support correct invocation. It doesn't explain what a valid 'preconditions' string looks like, what 'hunter_why' is for, or what the tool returns. The agent would need to inspect external docs or guess.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate, but it only mentions two of the five required parameters (impact, preconditions) and doesn't explain formats, constraints, or how they relate. engagement_id, card_id, and hunter_why are absent from the description, and the schema only labels them as strings. This leaves the agent to guess at their meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a concrete verb ('Record') with a specific resource ('a card') and identifies the payload ('hunter impact_class and preconditions'). It distinguishes itself from the sibling tools, none of which address recording impact state. However, it introduces the term 'impact_class' that doesn't appear in the schema ('impact'), and omits the other required fields from the description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus the siblings. It doesn't state prerequisites, whether it should be called before/after other tools like execute_proof or draft_waves, or any alternative to use instead. The only inference is from the verb 'record', but that's not enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
9 tool updates
v0.1.1- First observed
begin_engagement - First observed
draft_proof - First observed
draft_waves - First observed
engagement_health - First observed
execute_proof - First observed
execute_wave - First observed
query_cards - First observed
second_look - First observed
state_impact
TDQS
Scored across 9 tools
Each tool targets a distinct resource and action: engagements, waves, proofs, and cards are cleanly separated. The two execute tools are disambiguated by proof vs wave, and the two draft tools by waves vs proof, so an agent should not confuse them.
Most tools follow a clear verb_noun snake_case pattern like execute_proof, begin_engagement, draft_waves, and query_cards. engagement_health and second_look break the pattern by using noun phrases, but the overall convention remains readable and predictable.
Nine tools is a well-scoped size for this domain, covering engagement setup, wave and proof execution, and card interaction without unnecessary redundancy or bloat.
The core loop is represented, but there is no explicit admission step for drafted waves before execute_wave, and engagements lack update/close lifecycle operations. These are notable workflow gaps that could stall end-to-end operations.
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