L-Dopa
L-Dopa
Ein MCP-Server, der KI-Agenten hilft, sich zu erholen, neu zu fokussieren und was zu schaffen.
L-Dopa hat mich wieder hingekriegt, okay??
L-Dopa ist ein kleiner, produktionsorientierter Model Context Protocol (MCP) Server, der einem Agenten hilft, sich zu erholen, wenn ein Ansatz scheitert, Kontext verstreut ist oder Retries sich in einer Schleife verfangen. Er führt keine Befehle aus, verändert keine externen Systeme und ersetzt nicht das Urteilsvermögen eines Agenten. Er analysiert die ihm gelieferten Belege, behält einen begrenzten Recovery-Zustand und schlägt einen sichereren nächsten Schritt vor.
Der Name ist ein Witz. Die Recovery-Schleife nicht.
Was es tut
L-Dopa v0.1 bietet sechs MCP-Tools, die einem Agenten ermöglichen, Fehler zu diagnostizieren, den Umfang zu reduzieren, relevanten Kontext wiederherzustellen und Retries bewusst zu verwalten.
Tool | Verwendung | Rückgabe |
| Ein Vorgang ist fehlgeschlagen und der Agent hat einen Fehler- oder Logauszug. | Wahrscheinliche Ursache, kalibrierte Konfidenz, geschwärzte Belege, nächste Aktionen und Retry-Anleitung. |
| Der Agent kreist, ohne eine nützliche Beobachtung zu machen. | Ein knapper Reset, der sich auf eine Annahme und eine minimale sichere Prüfung konzentriert. |
| Eine Aufgabe ist zu breit oder verworren. | Eine priorisierte Liste von ein bis fünf konkreten nächsten Aktionen; drei ist die Standardeinstellung. |
| Der Agent benötigt relevanten Recovery-Kontext aus seiner L-Dopa-Sitzung. | Eine kompakte Zusammenfassung der letzten Fehler, Versuche, Erfolge, Fakten und ungelösten Probleme. |
| Der Agent erwägt oder meldet einen Retry. | Ein protokollierter, begrenzter Retry oder eine Sperre mit einer Empfehlung für eine alternative Strategie. |
| Der Agent ist festgefahren und möchte eine knappe Recovery-Sequenz. | Diagnose, fokussierte Aktionen, ein Recovery-Anstoß und Retry-Anleitung. |
Related MCP server: SentinelMCP
Designprinzipien
Prinzip | Umsetzung in v0.1 |
Keine magische Sicherheit | Die Diagnosekonfidenz ist |
Keine blinden Schleifen | Wiederholte materiell ähnliche Fehler, unveränderte Retry-Vorschläge und Retry-Limits pro Vorgang blockieren weitere Retries. |
Begrenzter Speicher | JSON-basierter Zustand behält nur die konfigurierte Anzahl von Datensätzen pro Kategorie für jede Sitzung. |
Standardmäßig sicher | L-Dopa bietet nur Diagnose und Planung. Es führt niemals Shell-Befehle oder externe Aktionen aus. |
Zugangsdatenbewusste Ausgabe | Häufige Muster für Token, Authorization-Header, Passwörter, API-Schlüssel, JWT, AWS-Schlüssel und GitHub-Tokens werden geschwärzt, bevor sie in Zustand, Logs und Tool-Ausgabe gelangen. |
Einfache Bereitstellung | Der Server nutzt den standardmäßigen MCP-stdio-Transport und erfordert Node.js 18 oder neuer. |
Installation
Klone das Repository und installiere die Abhängigkeiten:
git clone https://github.com/mshanghai570/L-Dopa.git
cd L-Dopa
npm install
npm run buildStarte den stdio-Server manuell mit:
npm startnpm start wartet absichtlich auf Eingaben. MCP-Server sprechen JSON-RPC über Standardeingabe und -ausgabe, daher startet normalerweise ein MCP-Client den Server für dich.
MCP-Client verbinden
Baue L-Dopa zuerst und verwende dann seinen ausführbaren Einstiegspunkt. Die folgende generische MCP-Konfiguration ist mit Clients kompatibel, die lokale stdio-Server unterstützen:
{
"mcpServers": {
"l-dopa": {
"command": "node",
"args": ["/absolute/path/to/L-Dopa/dist/index.js"],
"env": {
"LDOPA_STATE_FILE": "/absolute/path/to/l-dopa-state.json",
"LDOPA_MAX_HISTORY": "50",
"LDOPA_RETRY_LIMIT": "3",
"LDOPA_LOG_LEVEL": "info"
}
}
}
}Bei einem installierten Paket kann der Befehl je nach Client-Umgebung auch l-dopa lauten. Halte die Standardausgabe für MCP-Protokollmeldungen reserviert. L-Dopa schreibt seine eigenen kompakten, strukturierten Betriebsprotokolle in die Standardfehlerausgabe.
Konfiguration
L-Dopa läuft mit sicheren Standardeinstellungen und kann über eine JSON-Datei und/oder Umgebungsvariablen konfiguriert werden. Kopiere das bereitgestellte Beispiel, um loszulegen:
cp l-dopa.config.example.json l-dopa.config.json
LDOPA_CONFIG=./l-dopa.config.json npm startUmgebungsvariablen überschreiben Dateiwerte.
Einstellung | JSON-Eigenschaft | Umgebungsvariable | Standard | Bedeutung |
Zustandspfad |
|
|
| Speicherort des begrenzten JSON-Sitzungsspeichers. |
Verlaufslimit |
|
|
| Maximale positive Anzahl von Datensätzen, die pro Kategorie in einer Sitzung aufbewahrt werden. |
Retry-Limit |
|
|
| Maximale positive Anzahl geplanter/gemeldeter Retries, die für einen Vorgang aufbewahrt werden, bevor neue Retries blockiert werden. |
Logging-Stufe |
|
|
| Einer von |
Konfigurationsdatei | — |
| — | Optionaler Pfad zu einer JSON-Konfigurationsdatei. |
Die Konfiguration enthält keine Provider-Zugangsdaten-Einstellungen, da diese Version keine Modell- oder Provideraufrufe tätigt. Sollte eine zukünftige Erweiterung Zugangsdaten erfordern, übergib sie über Umgebungsvariablen; füge sie nicht zu einem Repository, einer Konfigurationsdatei oder einem Recovery-Prompt hinzu.
Tool-Referenz
Alle Textargumente sind in der Länge begrenzt und werden um Zugangsdaten geschwärzt, bevor L-Dopa sie speichert oder zurückgibt. sessionId ist standardmäßig "default", aber Agenten sollten für jede Aufgabe oder Konversation eine stabile ID verwenden, um zu verhindern, dass nicht zusammenhängende Recovery-Verläufe vermischt werden.
diagnose
Verwende diagnose nach einem Fehler mit so viel nützlichem Kontext wie verfügbar. errorMessage, recentOperation, logs, attemptedSolution, expectedResult und actualResult sind optional, aber eine präzise Fehlermeldung oder ein tatsächliches Ergebnis macht die Antwort nützlicher.
{
"sessionId": "deploy-2026-08-27",
"recentOperation": "Deploy version 0.1.0",
"errorMessage": "429 Too Many Requests",
"attemptedSolution": "Immediately retried the deployment",
"expectedResult": "Deployment accepted",
"actualResult": "The API rejected the request"
}Die Antwort enthält likelyCause, confidence, evidence, recommendedNextActions, retryAppropriate, tryDifferentStrategy und einen redacted-Indikator. Die Erkennung ist bewusst heuristisch und nicht irreführend autoritativ.
stimulate
Verwende stimulate, wenn ein Agent aufhören muss zu erzählen und anfangen muss zu lernen. Gib ein erforderliches task und einen optionalen context mit hohem Signalwert an.
{
"sessionId": "deploy-2026-08-27",
"task": "Repair the deployment",
"context": "The health check timed out twice after a successful build"
}Die Recovery-Strategie betont eine minimale, überprüfbare Aktion und warnt vor unveränderten Schleifen.
focus
Verwende focus, um eine breite Aufgabe in eine bewusst kurze Sequenz zu verwandeln. maxSteps ist optional und reicht von eins bis fünf; standardmäßig ist es drei.
{
"sessionId": "deploy-2026-08-27",
"task": "Repair the deployment and verify availability",
"context": "Health checks time out",
"maxSteps": 3
}reuptake
Verwende reuptake, wenn der Agent relevanten Sitzungskontext benötigt, ohne ein Transkript auszugeben. limit hat den Standardwert fünf und ist auf zwanzig begrenzt.
{
"sessionId": "deploy-2026-08-27",
"limit": 5
}Es gibt die aktuelle Aufgabe, den Recovery-Status, kürzliche Fehler, versuchte Lösungen, erfolgreiche Ansätze, entdeckte Fakten, ungelöste Probleme und die Anzahl der Retries zurück. Die v0.1-Tools stellen noch kein dediziertes Tool zum Aufzeichnen von Fakten bereit; discoveredFacts ist für Erweiterungen reserviert und bleibt im kompakten Schema vorhanden.
retry
Verwende retry, um einen expliziten Retry-Datensatz zu erstellen oder dessen Ergebnis zu melden. Ein proposedChange sollte benennen, was anders ist. L-Dopa erlaubt geplante, erfolgreiche und fehlgeschlagene Datensätze, führt den Retry selbst jedoch nie aus.
{
"sessionId": "deploy-2026-08-27",
"operation": "Deploy version 0.1.0",
"previousFailure": "429 Too Many Requests",
"proposedChange": "Wait for Retry-After and submit only one request",
"result": "planned"
}L-Dopa blockiert einen Retry, wenn das konfigurierte Vorgangslimit erschöpft ist, derselbe Fehler erneut aufgetreten ist oder ein bestehender Retry ohne eine vorgeschlagene Änderung wiederholt wird. Die Sperrantwort empfiehlt einen begrenzten alternativen Pfad, der auch darin bestehen kann, eine klar abgegrenzte Teilaufgabe und gesammelte Belege an einen anderen fähigen Agenten zu übergeben.
fix_me
Verwende fix_me für die Kurzversion von diagnose → focus → stimulate → Retry-Anleitung. Es zeichnet einen bereitgestellten Fehler auf, sofern vorhanden, und gibt dann einen Plan zurück; es führt ihn nicht aus.
{
"sessionId": "publish-0.1.0",
"task": "Publish the package safely",
"recentOperation": "npm publish",
"errorMessage": "401 Unauthorized",
"attemptedSolution": "Re-ran the same command"
}Zustand und Datenschutz
Der Zustandsspeicher ist eine einfache JSON-Datei, die atomar mit Modus 0600 geschrieben wird. Er hat eine versionierte Form und speichert separate Sitzungen mit sessionId als Schlüssel. Innerhalb jeder Sitzung werden Fehlerdatensätze, Retry-Datensätze, versuchte Lösungen, erfolgreiche Ansätze und entdeckte Fakten auf maxHistory begrenzt.
Der Zustand ist bewusst leichtgewichtig, kein Langzeitgedächtnis-System. Er ist lokal auf dem Rechner des ausführenden Benutzers und wird von L-Dopa nicht übertragen. Überprüfe oder lösche die konfigurierte Zustandsdatei, wann immer du den Recovery-Verlauf löschen möchtest.
Wichtig: Die Schwärzung deckt mehrere häufige Zugangsdatenmuster ab, ist aber eine Schutzmaßnahme und kein Freibrief, Geheimnisse einzureichen. Platziere nicht absichtlich Passwörter, Tokens, private Schlüssel oder vollständige Authorization-Header in diagnostischen Eingaben.
Entwicklung
npm install
npm run build
npm testDas Projekt ist bewusst modular:
src/
config.ts Configuration loading and validation
index.ts Executable stdio MCP entry point
logger.ts Structured, redacted standard-error logging
redaction.ts Credential-detection and output redaction
recovery.ts Diagnostic, focus, stimulation, and plan logic
server.ts MCP server and tool registrations
state.ts Bounded, atomic JSON session storage
types.ts Shared contracts
tests/
l-dopa.test.ts End-to-end MCP and state behavior testsTestabdeckung
Die automatisierte Suite verbindet einen echten MCP-Client und -Server über den In-Memory-Transport des SDKs. Sie deckt die Serverinitialisierung, die MCP-Tool-Erkennung, jedes Tool, die Zustandspersistenz, die begrenzte Aufbewahrung, Retry-Limits, unveränderte Retries, die Erkennung wiederholter Fehler und die Schwärzung von Zugangsdaten ab.
Führe sie aus mit:
npm testEinschränkungen und Roadmap
L-Dopa v0.1 verwendet deterministische Heuristiken, erkennt daher häufige Fehlerklassen, ist aber kein allwissender Debugger. Es hat bewusst keinen Vektorspeicher, keine Fernpersistenz, keine Modell-Provider-Integration und keine Befehlsausführungsfähigkeit. Es untersucht nicht die verborgene Gedankenkette eines Agenten; es arbeitet nur mit bereitgestelltem operativem Kontext.
Zukünftige Erweiterungen sollten diese Grenzen wahren: Füge ein Tool nur hinzu, wenn es einen klaren Recovery-Nutzen bietet, halte die Befehlsausführung in einer separaten, berechtigungsgesteuerten Komponente und halte den Zustand begrenzt und einsehbar.
Lizenz
Available Tools
6 toolsdiagnoseDiagnose a failureA
Analyze an error or failed operation, record it in bounded session state, and recommend safe next actions.
| Name | Required | Description | Default |
|---|---|---|---|
| logs | No | Relevant log excerpt. Credentials are redacted. | |
| sessionId | No | A stable identifier for this agent recovery session. | default |
| actualResult | No | What happened instead. | |
| errorMessage | No | The exact error message, if available. | |
| expectedResult | No | What should have happened. | |
| recentOperation | No | The operation that most recently failed. | |
| attemptedSolution | No | A solution already attempted. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With annotations providing only broad flags, the description adds meaningful behavioral detail: it records state in a bounded session and recommends rather than executes actions. This clarifies side effects and scope without contradicting the annotations.
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?
A single, tightly worded sentence front-loads the core verb and object, then packs in two meaningful behavioral outcomes. There is no filler or redundancy.
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 seven optional but fully documented parameters and no output schema, the description conveys the essential flow: analyze, record, and recommend. It stops short of describing the exact shape of the recommendation output, but it is sufficiently complete for an agent to understand the tool's role.
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 100%, and the input schema already documents each parameter, so the tool description does not need to repeat parameter details. The description adds no parameter-specific meaning beyond the schema, so the baseline of 3 is appropriate.
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 names a specific verb ('Analyze') and resource ('an error or failed operation'), and adds two concrete outcomes: recording in bounded session state and recommending safe next actions. It is clear about what the tool does, though it does not explicitly differentiate it from sibling tools like retry or fix_me.
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 'Analyze an error or failed operation' implies the intended trigger, and 'recommend safe next actions' positions it as an advisory step before acting. However, it gives no explicit guidance on when not to use it or how it compares to sibling alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fix_meCreate a complete recovery sequenceA
Combine diagnosis, focus, stimulation, and retry guidance into a concise recovery plan without executing commands.
| Name | Required | Description | Default |
|---|---|---|---|
| logs | No | Relevant log excerpt. Credentials are redacted. | |
| task | Yes | The task that is stuck or needs recovery. | |
| sessionId | No | A stable identifier for this agent recovery session. | default |
| actualResult | No | Actual result. | |
| errorMessage | No | The exact error message, if available. | |
| expectedResult | No | Expected result. | |
| recentOperation | No | The operation that failed. | |
| attemptedSolution | No | A solution already tried. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already tell the agent the operation is not read-only, not idempotent, and not destructive. The description adds valuable behavioral context beyond those annotations by explicitly stating that no commands are executed, meaning the tool only produces guidance/planning content. This helps the agent set expectations about 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, front-loaded sentence with no filler. It states the action, the inputs, the output, and a key behavioral constraint ('without executing commands') in under 15 words. Every phrase earns its place.
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 8-parameter schema with full coverage, annotations, and no output schema, the description provides enough context: it defines the output as a concise recovery plan and clarifies the non-execution behavior. It does not detail the plan's format or how to handle partially supplied inputs, but the required task parameter and well-described optional parameters make the tool usable.
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 100%, so all eight parameters already have descriptions. The tool description adds conceptual meaning by mapping the plan components to diagnosis, focus, stimulation, and retry guidance, but it does not explain how specific parameters map to those components. Baseline 3 is appropriate because the schema carries the parameter-documentation burden.
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 ('Combine') and resource ('diagnosis, focus, stimulation, and retry guidance into a concise recovery plan'), so an agent knows what the tool produces. It also distinguishes itself from the sibling tools diagnose, focus, stimulate, and retry by framing those as components rather than the tool's own action. The phrase 'without executing commands' further differentiates it from the more action-oriented siblings.
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 clearly conveys that this tool is for synthesizing a recovery plan rather than executing recovery actions, which gives the agent a clear decision context. It does not explicitly enumerate exclusions or say 'use X when...', but naming the component tools implies when this orchestrator is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
focusFocus the next actionsA
Reduce a complicated task to a short, prioritized sequence of concrete, verifiable next actions.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | The task to reduce into next actions. | |
| context | No | Relevant blocker, constraint, or diagnostic finding. | |
| maxSteps | No | Number of actions to return; defaults to three. | |
| sessionId | No | A stable identifier for this agent recovery session. | default |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide no positive behavioral hints (all false), so the description itself must convey behavior. It does state the transformation and output nature, but it does not disclose aspects like whether actions are executed, how sessions affect behavior, or any side effects. This is adequate but not richly transparent.
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?
A single, front-loaded sentence with no wasted words. It efficiently conveys the core purpose and expected output without redundancy.
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, the description does indicate the return shape ('sequence of concrete, verifiable next actions'), which helps. However, it lacks guidance on how the tool relates to the provided sessionId/context and does not address when it should be preferred over sibling tools, leaving some practical gaps.
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 100%, so the input schema fully documents all four parameters. The description adds no parameter-specific meaning beyond the schema, hitting the baseline expected when the schema carries the load.
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 names a specific verb ('reduce') and resource ('a complicated task') and states the output: 'a short, prioritized sequence of concrete, verifiable next actions.' This clearly communicates the tool's purpose, though it does not explicitly contrast it with siblings like diagnose or retry.
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 the tool should be used when a task is complicated and needs to be broken into manageable next actions. However, it gives no explicit when-to-use vs. alternative guidance and does not mention any exclusions or sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
retryPlan or record an intelligent retryA
Record a bounded retry with its specific change and result. Blocks unchanged or exhausted retry loops.
| Name | Required | Description | Default |
|---|---|---|---|
| notes | No | Short result or verification note. | |
| result | No | Whether the retry is planned, succeeded, or failed. | planned |
| operation | Yes | The operation to retry. | |
| sessionId | No | A stable identifier for this agent recovery session. | default |
| proposedChange | No | What will be changed before retrying. | |
| previousFailure | No | The previous failure for this operation. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only say the tool is not read-only, not idempotent, and not destructive. The description adds a real behavioral trait beyond those annotations: retries must be bounded, and unchanged or exhausted retry loops are blocked. This gives the agent useful expectations about side effects and constraints, with no contradiction against the annotations.
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?
Two sentences with no filler: the first states the core action and required components, the second states the key constraint. It is front-loaded and every phrase earns its place. The description is appropriately sized for what it conveys.
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 schema covers all parameters well and the description gives the core retry constraint, so the tool is basically usable. However, the title mentions 'Plan or record' while the description only explains recording, and there is no output schema or description of what happens when a retry is blocked or succeeds. The exact meaning of 'bounded' and the return behavior remain under-specified.
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 100%, and each parameter already has a meaningful description, so the baseline is 3. The description only echoes 'change and result' which maps to proposedChange/result, and it does not add operational detail about sessionId, previousFailure, or notes. It does not need to compensate for missing schema docs.
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?
States a specific action and resource: 'Record a bounded retry with its specific change and result.' It also adds the distinguishing guardrail—'Blocks unchanged or exhausted retry loops'—which separates it from sibling tools like diagnose or fix_me. The title's 'Plan or record' is somewhat broader, but the description still gives a clear, non-tautological purpose.
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 for retrying an operation after a change and warns against unchanged or exhausted retry loops, so there is some embedded when-not guidance. However, it never explicitly states the triggering conditions for using this tool over a sibling, nor does it name alternatives. The usage context remains mostly inferred rather than directly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
reuptakeRecover relevant session contextBRead-onlyIdempotent
Retrieve a compact, relevant summary of failures, attempts, facts, successes, and unresolved issues from this session.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum recent entries per context category. | |
| sessionId | No | A stable identifier for this agent recovery session. | default |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds useful output composition details (failures, attempts, facts, successes, unresolved issues) but does not disclose additional behavioral traits such as staleness, scoping caveats, or return formatting.
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?
One front-loaded sentence states the action, the resource, and the scope with no wasted words. It is appropriately sized for the tool's simplicity.
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 read-only, side-effect-free retrieval tool with fully documented parameters, the description gives an agent enough to call it and understand the returned content. It could briefly mention when to use it relative to siblings, but the essential invocation context is present.
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 input schema fully documents limit and sessionId with defaults, bounds, and descriptions, so schema coverage is 100%. The description adds no parameter-specific meaning or usage examples, keeping this at the baseline.
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 ('Retrieve') and clearly identifies the resource: a compact session summary containing failures, attempts, facts, successes, and unresolved issues. It is unambiguous about what the tool does, though it does not explicitly contrast with sibling tools like diagnose or focus.
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 reuptake versus its siblings such as diagnose, focus, or fix_me. The intended use case is only implied by the title and description, not explicitly stated as a condition or recommendation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
stimulateNudge a stuck agentA
Return a concise, structured recovery nudge that emphasizes reassessment, minimal checks, and changed approaches.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | The task on which the agent is stuck. | |
| context | No | The most relevant constraint, failed assumption, or observation. | |
| sessionId | No | A stable identifier for this agent recovery session. | default |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description is transparent about the primary behavior: it returns a nudge rather than performing the task, and it specifies the nudge's 'concise, structured' form and themes. With no output schema and annotations that provide no positive safety signal, this is meaningful disclosure, though side effects and session-state behavior are not addressed.
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?
A single sentence that front-loads the action ('Return'), the deliverable ('recovery nudge'), and the key content directives. Every phrase earns its place, with no filler or duplicated schema 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?
The definition is adequate for a simple text-generation tool with fully documented parameters and a clear title. However, it lacks explicit sibling differentiation, usage timing, and an output-format preview, so an agent choosing among the six siblings gets only partial 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 description coverage is 100%, with clear descriptions for task, context, and sessionId, so the baseline of 3 applies. The description's mention of reassessment and changed approaches hints at how context might be used but adds no concrete parameter-level meaning beyond 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 names a specific deliverable—a 'concise, structured recovery nudge'—and defines its content emphasis (reassessment, minimal checks, changed approaches), so an agent knows what the tool produces. It does not explicitly distinguish this from sibling tools like diagnose or retry, so it falls short of a 5.
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?
Usage context is implied rather than stated: the title and the task parameter describe an agent that is stuck, and context mentions failed assumptions. However, the description never says when to prefer stimulate over diagnose, reuptake, focus, retry, or fix_me, nor does it offer exclusion conditions.
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.
6 tool updates
v0.1.0- First observed
diagnose - First observed
fix_me - First observed
focus - First observed
retry - First observed
reuptake - First observed
stimulate
TDQS
Scored across 6 tools
Several tools have unclear boundaries: diagnose, focus, and fix_me all produce recommended next actions or plans, while stimulate and focus both generate recovery-oriented output. Only reuptake and retry are clearly distinct, making tool selection genuinely ambiguous.
The tools mostly follow a consistent lowercase imperative-verb style: diagnose, stimulate, focus, retry. The main deviation is fix_me, which introduces an underscore and an object pronoun, but the overall naming pattern is still recognizable.
Six tools is a reasonable size for a session-recovery-focused server. However, fix_me is an aggregate of other tools and adds some redundancy, so the set is slightly less lean than it could be.
The tool surface covers the core recovery loop well: diagnose failures, focus on next actions, retry with bounded state, and retrieve session context. Minor gaps exist around explicit session reset or state-clearing operations, but agents can generally work around them.
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