MCP-Server für die Productive.io API v2 – Projekte, Aufgaben, Zeiterfassung, Ressourcenplanung, Finanzen, CRM und Berichte für eine Organisation.
Productive stellt etwa 650 Operationen über 132 Ressourcen bereit. Diese in 650 MCP-Tools zu verwandeln, würde die Tool-Liste jedes Clients überfluten, daher ist dieser Server zwölf Tools, gesteuert von einer generierten Registry: Die Tools sind generisch, und die Registry weiß, was jede Ressource tatsächlich akzeptiert.
Discovery – productive_search_capabilities, productive_describe_resource, productive_check_connection, productive_describe_custom_fields
Read – productive_list (Filter, Sortierungen, Includes, Paginierung und die 26 Berichts-Endpunkte mit Gruppierung), productive_get
Write – productive_create, productive_update, productive_delete, productive_run_action (150 benannte Verben: archive, restore, approve, close, copy, finalize, send…), productive_track_time, productive_commit_operation
Per-user auth (opt-in) – productive_connect, productive_status, productive_disconnect – siehe Authentifizierung
Beginnen Sie mit productive_search_capabilities. Die Ressourcennamen von Productive sind eigenwillig – ein Budget ist ein deal, eine Board-Spalte ein workflow_status, eine Zeiterfassungs-Genehmigung lebt auf time_entries – und Raten kostet Aufrufe.
Related MCP server: productive-mcp-rb2
src/productive/registry.generated.ts wird aus dem veröffentlichten OpenAPI-Dokument von Productive von scripts/generate-registry.mjs abgeleitet und eingecheckt, sodass CI nie das Netzwerk benötigt und eine Spezifikationsänderung als überprüfbarer Diff erscheint. Für jede Ressource erfasst es die Filterfelder, Sortierschlüssel, Berichts-Gruppenschlüssel, einbindbaren Beziehungen, die beschreibbaren Attribute für Create und Update mit den als erforderlich markierten sowie jede benannte Aktion.
Das ist es, was zwölf Tools ehrlich bleiben lässt. productive_describe_resource gibt den genauen Vertrag für eine Ressource zurück, und jedes Argument wird dagegen geprüft, bevor eine Anfrage rausgeht.
Regenerieren Sie mit npm run registry:generate (fügen Sie ein Pfadargument hinzu, um eine lokale Kopie der Spezifikation zu verwenden). Der Generator lässt den Build fehlschlagen, wenn eine handgeschriebene Klassifizierung – eine Risikostufe, ein nach außen gerichtetes Flag, eine blockierte Operation – keinem Pfad in der Spezifikation mehr entspricht, sodass eine Umbenennung upstream nicht stillschweigend eine Schutzmaßnahme entfernen kann.
Alles hier wurde gegen eine Live-Organisation verifiziert, weil sich Spezifikation und API an Stellen unterscheiden, die wichtig sind.
Zeit ist Minuten. Geld ist Untereinheiten. Ein Zeiteintrag von 2 sind zwei Minuten. Beides kommt auch so zurück.
Unbekannte Filter, Sortierungen und Includes schlagen laut fehl. HTTP 400 mit unsupported_filter, sort_param_unsupported, unsupported_include. Sie hier zu validieren ist also ein besserer Fehler, kein Sicherheitsnetz.
Unbekannte Schreibattribute schlagen still fehl. PATCH mit einem falsch geschriebenen Attribut gibt HTTP 200 zurück und ändert nichts – nicht von Erfolg zu unterscheiden. Dieser Server lehnt daher ein Attribut ab, das die Ressource nicht deklariert, anstatt einen Schreibvorgang zu melden, der nicht stattgefunden hat. Das ist das mit Abstand Nützlichste, was die Registry tut.
Es gibt genau sechs Filteroperatoren, auf jedem Feld: contains, eq, gt, lt, not_contain, not_eq. Die Spezifikation listet vier pro Feld auf und lässt gt/lt aus, die aber funktionieren; gte, lte, in, not_in, starts_with, ends_with, blank und present werden alle mit unsupported_filter_operation abgelehnt. Es gibt keinen inklusiven Vergleich, daher benötigt ein inklusiver Bereich die eigenen after/before- oder <field>_after/<field>_before-Filterfelder der Ressource.
page[size] ist auf 200 begrenzt und wird still gekappt. Eine Anfrage für 500 liefert 200 ohne Fehler zurück. Ergebnisse enthalten total und nextPage, damit eine Seite nicht für die gesamte Antwort gehalten wird.
PATCH ist wirklich partiell. Ausgelassene Attribute behalten ihre Werte; es besteht keine Notwendigkeit, einen ganzen Datensatz erneut zu senden.
data.type wird nicht geprüft. Das Patchen einer Aufgabe mit type: "projects" gelingt und wendet die Änderung an. Dieser Server sendet den korrekten Typ trotzdem.
Eine 403 mit der Aussage, die Organisations-ID „muss angegeben werden“, kann bedeuten, dass sie falsch war, nicht fehlend. Derselbe Code no_organization_id deckt einen fehlenden Header und eine Organisation ab, die das Token nicht erreichen kann.
Eine fehlende Funktion antwortet mit 404, nicht 403. /boards liefert 404 bei einer Organisation ohne diese Funktion, was sich wie ein defekter Pfad liest.
Löschungen können wiederherstellbar sein. Eine gelöschte Aufgabe erscheint in deleted_items mit item_type und item_id und kann über die restore-Aktion dieser Ressource wiederhergestellt werden. Nur für Aufgaben verifiziert – gehen Sie nicht davon aus, dass es für jeden Typ gilt.
GET /users ist der einzige auf den Aufrufer bezogene Endpunkt. Er gibt genau einen Datensatz zurück – Sie – und so identifiziert dieser Server den Besitzer eines Tokens. Es gibt kein /users/me; dieser Pfad liefert 404. Vorsicht bei /organization_memberships: Es ist nicht auf die angeheftete Organisation beschränkt, sondern listet die Mitgliedschaften des Aufrufers in jeder Organisation, zu der er gehört, daher ist seine Zeilenzahl keine Mitarbeiterzahl.
Keine Rate-Limit-Header. Nur x-request-id, den Fehler dieses Servers zitieren. Bei 429 zurückziehen, anstatt das Limit auszuloten.
Vier Schalter, alle standardmäßig aus. Ein schreibgeschützter Server ist der nützliche, sichere Standard.
Schalter | Umfasst |
| Master-Schalter. Ohne ihn wird nichts verändert. |
| Geld, Preisgestaltung, Gehaltsabrechnung, Dokumente, die ein Kunde erhält: Rechnungen, Einzelposten, Zahlungen, Eingangsrechnungen, Ausgaben, Bestellungen, Angebote, Verträge, Preise, Tarifkarten, Gehälter, Gemeinkosten, Steuersätze, Bankkonten, Tochtergesellschaften. |
| Zugriff und organisationsweite Konfiguration: Personen, Mitgliedschaften, Berechtigungssätze, Teams, Einladungen, benutzerdefinierte Felder, Webhooks, Integrationen, Genehmigungs- und Zeiterfassungsrichtlinien. |
| Löschungen, zusätzlich zur Stufenfreigabe. |
Ein einziger Master-Schalter reicht für Productive nicht aus: Dieselbe API verschiebt eine Aufgabe, stellt eine Rechnung aus und gewährt einen Berechtigungssatz – das sind drei verschiedene Entscheidungen. Ein Server, dem Projektarbeit anvertraut ist, sollte dadurch nicht auch eine Rechnung senden können.
PRODUCTIVE_ALLOWED_RESOURCES / PRODUCTIVE_DENIED_RESOURCES grenzen eine Instanz weiter ein und gelten auch für Lesezugriffe – eine auf Zeiterfassung beschränkte Instanz sollte auch keine Gehälter lesen können.
Nie ausgesetzt, unabhängig von den Schaltern: passwords, sessions, organization_subscriptions, die nicht authentifizierten public/*-Freigabelinks und PATCH /users/{id}/update_password. Diese fehlen in der Registry, anstatt abgesperrt zu sein, sodass kein Policy-Fehler sie wieder öffnen kann.
Gewöhnliche Projektarbeit – eine Aufgabe, ein Zeiteintrag, eine Buchung, ein Kommentar – wird in einem einzigen Aufruf geschrieben. Für jeden Zeiteintrag einen Handshake zu verlangen, würde den Server für das unbrauchbar machen, was Menschen am häufigsten tun.
Alles mit einem größeren Wirkungskreis ist gestaffelt: Das Tool gibt die exakte Anfrage plus einen Hash zurück und sendet nichts, und productive_commit_operation führt sie nur aus, wenn die Operation unverändert zurückkommt. Das umfasst die Finanz- und Admin-Stufen, jedes Löschen, alles, was die Organisation verlässt, und jede bulk_*-Aktion – diese wirken auf alle Datensätze, die ein Filter findet, und weigern sich daher auch, ohne expliziten Filter ausgeführt zu werden.
Sechs Operationen sind als outward gekennzeichnet, weil sie im Moment ihrer Ausführung jemanden außerhalb der Organisation erreichen: invoices.send, invoices.send_einvoice, people.invite, people.resend, organizations.resend_code und das Erstellen einer invitation.
Zwei Modi. PRODUCTIVE_ORGANIZATION_ID ist in beiden erforderlich und ist nie ein Tool-Argument.
Jede Person verknüpft ihr eigenes Productive-Token, sodass Productive ihre Berechtigungen anwendet und ihren Namen bei dem festhält, was sie tut.
Das ist bei Productive wichtiger als bei den meisten Systemen. Productive schreibt Arbeit Personen zu: Ein Zeiteintrag gehört zu einer person_id, und jede Änderung wird im Aktivitätsprotokoll mit dem Besitzer des Tokens gestempelt – das ist der Nachweis, mit dem eine Kundenrechnung verteidigt wird. Bei einem gemeinsamen Token sagt dieses Protokoll, dass das Dienstkonto alles getan hat.
PRODUCTIVE_PER_USER_AUTH=true
PRODUCTIVE_TRUST_FORWARDED_USER=true
PRODUCTIVE_ENCRYPTION_KEY=<min 16 chars>
PRODUCTIVE_STORE_PATH=/data/store.json
PRODUCTIVE_PUBLIC_BASE_URL=https://productive.example.com
# PRODUCTIVE_API_TOKEN deliberately unsetDer Ablauf:
Der Aufrufer führt productive_connect aus und erhält einen Einmal-Link, 10 Minuten gültig, der an seine Identität gebunden ist.
Sie öffnen ihn und fügen ein Token ein, das sie in Productive unter Settings → API integrations erstellt haben. Das Token geht von ihrem Browser direkt zum Server, sodass es nie in das Gesprächstranskript gelangt – ein Productive-Token ist bearer-äquivalent zu ihrem gesamten Konto und erreicht, wie unten gemessen, häufig mehr als eine Organisation.
Bevor es gespeichert wird, ruft der Server GET /users mit diesem Token und der ID dieser Organisation auf. Ein Aufruf beweist drei Dinge: Das Token ist gültig, es kann diese Organisation erreichen, und wem es gehört. Die Seite bestätigt dann, welches Konto verknüpft wurde.
Tokens werden im Ruhezustand mit AES-256-GCM verschlüsselt, eine Zeile pro verifizierter Identität.
Stolperfallen:
Identität kommt nur vom Gateway. X-MCP-User wird nur gelesen, wenn PRODUCTIVE_TRUST_FORWARDED_USER=true ist, niemals von etwas, das der MCP-Client kontrolliert. Aktivieren Sie es nur hinter einem Gateway, das den Header aus einem validierten Token setzt und eine vom Client gelieferte Kopie entfernt – andernfalls kann ein Aufrufer eine beliebige Identität angeben und als sie handeln.
Kein Fallback. Ein nicht eingeschriebener Aufrufer erhält NOT_CONNECTED, niemals das gemeinsame Token, selbst wenn PRODUCTIVE_API_TOKEN zufällig gesetzt ist. Ein Fallback würde ihnen geliehene Rechte aushändigen, was genau das Versagen ist, das dieser Modus beseitigen soll.
/productive/enroll muss vom Browser des Benutzers erreichbar sein, unter Umgehung des MCP-Gateways – ein Browser kann das Bearer-Token des Gateways nicht tragen. Leiten Sie /productive/* auf PRODUCTIVE_PUBLIC_BASE_URL direkt zum Container. Seine Sicherheit ist das einmalige, identitätsgebundene State-Token.
Persistieren Sie PRODUCTIVE_STORE_PATH auf einem Volume, und halten Sie PRODUCTIVE_ENCRYPTION_KEY stabil – ändern Sie ihn, und jedes gespeicherte Token wird unentschlüsselbar.
Wenn die eigene Productive-E-Mail eines Tokens von der Verzeichnisadresse des Aufrufers abweicht, wird das laut auf der Seite und in productive_status gemeldet und trotzdem verbunden. Setzen Sie PRODUCTIVE_REQUIRE_EMAIL_MATCH=true, um stattdessen abzulehnen. Es ist standardmäßig aus, weil jeder, der das Token einer anderen Person einfügt, es bereits besitzt, sodass Ablehnen wenig Sicherheit bringt, während ein Productive-Konto unter einer anderen Adresse durchaus plausibel ist.
Die Pro-Benutzer-Authentifizierung trennt Berechtigungen und Zuordnung, nicht Organisationen. Die Organisations-P
PRODUCTIVE_TRUST_FORWARDED_USER hilft auch hier weiter: personenbezogene Schreibvorgänge (ein Zeiteintrag, eine Buchung) werden standardmäßig dem aufgelösten Aufrufer zugeordnet und nicht dem Inhaber des Tokens, und der Server weigert sich zu raten, wenn die Adresse niemandem oder mehr als einer Person entspricht. productive_check_connection nennt den Inhaber des Tokens in jedem Fall, sodass die Zuordnung nie eine Überraschung ist.
Eine Instanz bedient genau eine Organisation. X-Organization-Id stammt aus der Umgebung und ist nie ein Tool-Argument, sodass kein Codepfad — einschließlich der generischen Tools — einen anderen Mandanten erreichen kann. Führen Sie für eine zweite Organisation eine zweite Instanz aus; das Image ist dasselbe.
Das ist nicht theoretisch. Ein einzelnes Token erreicht routinemäßig mehrere Organisationen: Auf dem Konto, gegen das dies entwickelt wurde, lieferte GET /organizations drei zurück, und es genügte, nur den Header umzuschalten, um zwischen ihnen zu wechseln (die anderen beiden antworteten mit 403 subscription_expired, nicht mit „not found"). Der Header ist die gesamte Grenze, weshalb er festgelegt und nicht übergeben wird — und weshalb ein registriertes benutzerbezogenes Token gegen diese Organisation verifiziert wird, bevor es gespeichert wird.
Siehe .env.example. Die beiden erforderlichen Variablen sind PRODUCTIVE_API_TOKEN (Einstellungen → API-Integrationen in Productive; es erbt die Berechtigungen des erstellenden Benutzers) und PRODUCTIVE_ORGANIZATION_ID (die numerische ID in Ihrer Productive-URL).
Setzen Sie PRODUCTIVE_AUDIT_LOG, damit pro Mutationsversuch eine JSON-Zeile angehängt wird, einschließlich derer, die die Richtlinie abgelehnt hat. Anfragekörper werden bewusst nicht aufgezeichnet: sie enthalten Gehälter, Tarife und personenbezogene Daten, und ein Prüfprotokoll, das so streng geschützt werden muss wie das Quellsystem, wird in der Regel nicht gelesen.
npm install
npm run dev # stdio
npm run dev:http # streamable HTTP on :3000/mcp (stateless), /healthz open
npm test
npm run smoke:live # reads a real organization; stages one write, commits nothingDocker-Images: ghcr.io/borgels/mcp-server-productive (werden bei Push auf main veröffentlicht).
Apache-2.0.
Verify the token and organization and report what this deployment may do. Also names the person the token belongs to — every change Productive records is credited to them, whoever asked — and the caller identity, when a gateway forwards one.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already signals safety, and the description adds relevant behavioral context: token identity is attributed to every recorded change, and caller identity may be forwarded by a gateway. This goes beyond the annotation without contradicting it.
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 two sentences with the core action first, followed only by consequential detail about attribution and caller identity. There is no fluff or repetition of the title.
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 parameterless tool, the description conveys the main outputs: deployment capabilities, token owner, and caller identity. It does not specify exact response formatting or failure behavior, but the tool's simplicity and readOnly annotation make this a minor gap.
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 tool has zero parameters and the empty schema fully describes the input surface, so there is nothing for the description to add about parameter semantics. The baseline of 4 applies.
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 opens with a specific verb and resource: 'Verify the token and organization' and states what the tool reports (deployment capabilities, token owner, caller identity). This makes its role distinct from sibling data-operation tools like productive_get or productive_create.
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 establishes the context — validating the Productive token and organization and surfacing what the deployment can do — so an agent knows to call it for connection/auth checks. It does not explicitly name alternatives or when-not conditions, but the absence of parameters and the verification purpose make the intended use obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Execute a write that a prepare step staged. Pass the operation object back exactly as it was returned: it is hashed, and an altered operation is refused rather than run. Show the change to the person who asked for it before committing.
| Name | Required | Description | Default |
|---|---|---|---|
| operation | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the minimal readOnlyHint=false annotation, the description discloses two important behaviors: the operation is hashed and altered operations are refused, and the change must be shown to the requester before committing. This is valuable safety-relevant context for an agent.
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?
Three short, purposeful sentences with no fluff. Purpose, usage constraint, and mandatory approval step are all front-loaded and directly actionable.
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 description covers what matters most for a commit operation: exact object reuse, hash enforcement, and user approval. It does not describe the success response or explicitly name a prepare tool, but the workflow is sufficiently constrained for correct invocation.
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 has 0% description coverage, and the single nested 'operation' parameter has no field-level documentation. However, the description compensates by instructing the agent to pass the exact object back and explaining the hash invariant, which materially reduces the risk of misuse despite lacking field-by-field semantics.
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?
Description states a specific verb and resource: 'Execute a write that a prepare step staged.' This clearly identifies the tool as the commit step for a staged operation, distinguishing it from direct create/update/delete siblings by the staged-operation framing.
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?
Gives clear usage context: use after a prepare step, pass the operation object back exactly as returned, and show the change before committing. It does not explicitly name alternatives or say when not to use it, but the staged-operation workflow is unmistakable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a record on any writable resource. Required attributes are checked first, and so is every attribute name. On a financial or administrative resource this returns a staged operation to review rather than creating anything — pass it to productive_commit_operation to go through with it.
| Name | Required | Description | Default |
|---|---|---|---|
| resource | Yes | ||
| attributes | No | Attributes to write, by their Productive names. Validated against the resource contract first, because Productive answers 200 and ignores an unknown attribute rather than reporting it. Custom-field values go in a nested `custom_fields` object keyed by field id. | |
| relationships | No | JSON:API relationships, where the resource accepts them. Most links are set through an `_id` attribute instead. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only mark readOnlyHint=false, so the description carries the burden of explaining mutation nuance. It discloses the staging behavior, the validation-before-write behavior, and the fact that commit is required on certain resources. This is meaningful behavioral context beyond the schema.
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?
Three sentences, no filler, with the core action first, then validation, then the exception case. Every sentence adds actionable 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?
For a create tool with no output schema, it explains the essential invocation path, including when commit is required. It could say more about return shape or failure behavior, but the description and schema together cover the necessary details.
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 already describes attributes and relationships in detail, covering 2 of 3 parameters. The tool-level description adds no parameter-specific detail; `resource` remains minimally described, though it is self-evident and explained at a high level as 'any writable resource.'
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 exactly what it does: create a record on any writable resource. The explicit mention of productive_commit_operation distinguishes it from update/delete/list/get siblings. No ambiguity about the operation performed.
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?
Explains the resource-type condition that changes behavior: financial/administrative resources stage an operation rather than creating, and directs the agent to productive_commit_operation to finalize. It doesn't enumerate exclusions versus update/delete, but for a create tool this is sufficient guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete a record. Needs its own switch beyond ordinary writes, and always returns a staged operation first. Many Productive types are meant to be archived rather than deleted — check productive_describe_resource for an archive action before reaching for this.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| resource | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare destructiveHint=true and readOnlyHint=false, so the description need not restate that. It adds non-obvious behavioral facts: deletion requires a separate switch and always returns a staged operation first. This goes beyond annotations and meaningfully sets expectations.
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 three concise sentences with the core purpose first, followed by two high-value caveats. Every sentence earns its place; there is no filler or repetition of schema/annotations.
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 destructive tool with two simple parameters and no output schema, the description provides essential behavior: staged operation first, archive exception, and switch requirement. The main gap is parameter semantics, but the operational guidance is strong enough to make the tool safely invocable.
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 define the `resource` or `id` parameters. 'Delete a record' hints that resource is the record type and id is its identifier, but this is too vague to clarify valid resource types or how the id is used. The description adds minimal value 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 states a specific action ('Delete') and resource ('a record'), making the tool's purpose clear. It also distinguishes the tool from create/update operations and explicitly references the archive alternative, differentiating it from a sibling behavior.
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?
It explicitly instructs the agent to check productive_describe_resource for an `archive` action before using delete, giving clear when-to-use-vs-alternative guidance. It also notes the requirement for a special switch beyond ordinary writes, which is a practical precondition for invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the organization's custom fields with the id each value is written under, the object it applies to, and its options. Custom-field values are keyed by numeric id, not by display name — a name used as a key is accepted with 200 and stored nowhere.
| Name | Required | Description | Default |
|---|---|---|---|
| appliesTo | No | Narrow to fields on one object, matched against Productive's own wording for it. That wording is not this server's resource key — fields on people report "employees" — so call this without an argument first and pick from appliesToValues. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint annotation, the description discloses a critical behavioral quirk: custom-field values are keyed by numeric id, not display name, and using a name as a key is silently accepted with 200 but stored nowhere. This is exactly the kind of non-obvious behavior that prevents an agent from making a subtle mistake.
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 two sentences with no filler. It front-loads the purpose and return fields, then adds the essential behavior warning. Every sentence 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?
For a read-only listing tool with one optional parameter, the description is complete: it names the resource, lists the output components, and flags the keying gotcha. The absence of an output schema is mitigated by the explicit statement of what is returned, and the readOnlyHint annotation covers safety expectations.
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 has 100% description coverage and already explains appliesTo in detail, including the 'employees' example and the instruction to call without an argument first. The tool description itself adds no additional parameter semantics 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 uses a specific verb ('List'), names the resource ('organization's custom fields'), and specifies what is returned: id, object, and options. It clearly distinguishes this from siblings like productive_describe_resource, which would describe a resource's schema rather than enumerate custom fields.
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 its usage context clearly: call it to list custom fields and understand how values are keyed. The input schema adds explicit guidance to call without an argument first and then pick from appliesToValues, which is practical usage direction. It does not explicitly name alternative tools or state when not to use it, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The exact contract for one resource, derived from Productive's own OpenAPI document: every filter field, the operators any field accepts, sort keys, report group keys, includable relationships, the writable attributes for create and update with the required ones marked, and any named actions. Read this before writing — it is the difference between a write that lands and a 200 that changed nothing.
| Name | Required | Description | Default |
|---|---|---|---|
| resource | Yes | Resource key, e.g. "tasks", "time_entries", "reports/time_reports". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds valuable behavioral context: output is derived from Productive's own OpenAPI document and includes specific contract elements, plus a warning about write no-ops that goes beyond the annotation.
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 dense but efficient: one sentence enumerates the full contract contents, and a second short sentence delivers a high-impact usage warning. Every phrase contributes meaning, with no filler or repetition of the schema.
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 simple one-parameter read-only introspection tool, the description fully specifies what output the agent should expect and when to call it. The lack of an output schema is compensated by the detailed enumeration of returned contract components.
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 schema already documents the single 'resource' parameter with examples. The description adds no additional syntax, validation, or format details for the parameter, so it stays at the baseline where the schema does the heavy lifting.
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 precise verb-object relationship: it provides 'the exact contract for one resource' and enumerates the concrete contents such as filter fields, operators, sort keys, and writable attributes. This clearly distinguishes it from siblings like productive_get, productive_list, and productive_describe_custom_fields.
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 explicitly tells the agent to 'Read this before writing' and contrasts a successful write with 'a 200 that changed nothing,' giving a clear trigger for use. It does not explicitly name alternative tools or when not to use it, but the intended context is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read one record by id, optionally pulling related records in the same call rather than following relationship stubs one at a time.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| raw | No | ||
| include | No | ||
| resource | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already establishes safety, and the description adds meaningful behavior: optional eager-loading of related records in the same call. It does not explain raw mode or default include behavior, but the core read semantics are transparent and align with 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?
The description is a single, well-structured sentence that front-loads the core purpose and then adds the key optional behavior. There is no redundant or filler language.
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 and no parameter descriptions, the description is too sparse to fully support correct invocation. The agent still has to guess what 'resource' values are valid, what 'raw' does, and what the response shape looks like. The relationship-include guidance is helpful but not enough.
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 compensate for all four parameters. It explains the purpose of 'id' and loosely hints at 'include', but it never clarifies 'resource' or 'raw', which are both undocumented in the schema and the description. This is a significant gap.
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 ('Read') and clear object ('one record by id'), which distinguishes it from sibling tools like productive_list and productive_create. The added phrase about pulling related records also separates it from naive relationship-stub traversal.
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 conveys when to use the tool: when you need a single record by id and optionally want related records in one call rather than following stubs. It does not explicitly exclude alternatives like productive_list for multiple records, so it stops short of full when-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List any Productive resource with server-side filtering, sorting, includes and paging. Also how the 26 report endpoints are read: use resource "reports/time_reports" (and the rest) with group to get aggregated rows instead of records. Results are flattened out of JSON:API — attributes hoisted, included records inlined into their relationship — with total and nextPage so a page is never mistaken for the whole answer.
| Name | Required | Description | Default |
|---|---|---|---|
| raw | No | Return Productive's JSON:API envelope untouched instead of flat records. | |
| page | No | ||
| sort | No | Sort keys; prefix with "-" for descending, e.g. ["-created_at"]. | |
| group | No | Report aggregation key. Only the reports/* resources accept one. | |
| filters | No | Server-side filters. A bare value means equality: {"assignee_id":"5"}. An array is comma-joined: {"id":["1","2"]}. An operator object narrows further: {"created_at":{"gt":"2026-01-01"}} — the ONLY operators Productive accepts are contains, eq, gt, lt, not_contain, not_eq. There is no gte/lte, so for an inclusive date range use the resource's own after/before filter fields instead. Logical groups nest: {"$op":"and","0":{"assignee_id":{"eq":"5"}},"1":{"company_id":{"eq":"7"}}}. Field names are validated against the resource — call productive_describe_resource for the list. | |
| include | No | Relationships to pull in the same call, e.g. ["assignee","project"]. | |
| resource | Yes | One of 132 resource keys; see productive_search_capabilities. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant behavioral detail beyond the readOnlyHint annotation: results are flattened out of JSON:API, attributes are hoisted, included records are inlined, and responses include total and nextPage so the agent understands pagination is partial. It also clarifies that report resources return aggregated rows rather than records. This goes well beyond the minimal safety signal.
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 concise at three sentences and front-loads the core purpose. Each sentence provides meaningful information, though the phrase 'Also how the 26 report endpoints are read' is slightly awkward and could be streamlined. Overall it is efficient without unnecessary detail.
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 explains the return shape well: flattened records, inlined relationships, total, and nextPage. It also covers the special report behavior and aggregated rows. For a complex 7-parameter listing tool with 132 possible resources, this is sufficiently complete to guide correct invocation.
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 86%, so the input schema already documents most parameters well. The description adds a little context around group for report endpoints, but it mostly summarizes capabilities like filtering and paging rather than meaningfully extending the schema's parameter documentation. Baseline 3 is appropriate because the schema carries the parameter-semantic 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 clearly states the tool lists any Productive resource with filtering, sorting, includes, and paging. It also explicitly handles the special behavior of the 26 report endpoints, distinguishing this from siblings like productive_get and productive_search_capabilities. The verb 'List' and resource scope are unambiguous.
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 gives clear guidance on when to use this tool, including the specific instruction to use resource 'reports/time_reports' with group for aggregated report rows. It provides the main usage context but does not explicitly enumerate exclusions or alternatives such as 'use productive_get for a single record.' The context is clear enough that an agent can choose correctly in most cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run one of Productive's named verbs: archive, restore, close, open, approve, reject, copy, finalize, send, reposition and the rest. Actions named bulk_* act on every record the filter matches rather than one, and refuse to run without an explicit filter. productive_describe_resource lists the actions a resource has.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Required for actions that act on one record. | |
| action | Yes | Action name, e.g. "archive", "approve", "send". | |
| filters | No | Server-side filters. A bare value means equality: {"assignee_id":"5"}. An array is comma-joined: {"id":["1","2"]}. An operator object narrows further: {"created_at":{"gt":"2026-01-01"}} — the ONLY operators Productive accepts are contains, eq, gt, lt, not_contain, not_eq. There is no gte/lte, so for an inclusive date range use the resource's own after/before filter fields instead. Logical groups nest: {"$op":"and","0":{"assignee_id":{"eq":"5"}},"1":{"company_id":{"eq":"7"}}}. Field names are validated against the resource — call productive_describe_resource for the list. | |
| resource | Yes | ||
| attributes | No | Attributes to write, by their Productive names. Validated against the resource contract first, because Productive answers 200 and ignores an unknown attribute rather than reporting it. Custom-field values go in a nested `custom_fields` object keyed by field id. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only provide readOnlyHint=false, so the description carries the behavioral burden. It discloses two non-obvious behaviors: bulk_* actions operate on every record the filter matches, and they refuse to run without an explicit filter. The schema also surfaces that Productive answers 200 and silently ignores unknown attributes, adding important behavioral context beyond the annotation.
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 three sentences with no filler. It front-loads the core purpose, then immediately covers the most dangerous edge cases (bulk scope and filter requirement), and ends with a useful discovery pointer. Every sentence 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?
For a complex 5-parameter tool with nested filter objects and no output schema, the description and schema together cover the essential invocation details: valid action classes, bulk semantics, filter requirements, field validation, and discovery via productive_describe_resource. The only notable gap is the lack of any mention of what the response looks like or error behavior beyond the silent-attribute-ignore note, which is a minor omission for an action-triggering tool.
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 roughly 80%, and the schema already provides rich semantics for filters, attributes, and id. The description adds the bulk-vs-single distinction and the explicit-filter requirement, which complements the filters parameter. Given this high schema coverage, the baseline of 3 is appropriate; the description does not need to re-document each parameter.
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 ('Run') and a clear resource class ('one of Productive's named verbs'), then lists concrete examples: archive, restore, close, open, approve, reject, copy, finalize, send, reposition. It also distinguishes single-record actions from bulk_* actions, so the tool's purpose is unmistakable and well differentiated from the sibling CRUD tools.
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 states that bulk_* actions require an explicit filter and calls out productive_describe_resource as the way to discover which actions a resource supports. It does not, however, give explicit when-not-to-use guidance against siblings like productive_update or productive_delete, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find the right resource, tool or guide for a subject. Start here: Productive names its resources its own way (a budget is a deal, a person is a person, a board column is a workflow_status), and guessing wastes calls. Returns matching tools, workflow guides, and resources with the operations each supports.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | Subject to search for, e.g. "invoice", "capacity", "custom field", "time". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint=true already in annotations, the description adds useful behavioral context: it returns matching tools, workflow guides, and resources along with the operations they support. It also discloses the important quirk that resource names may not match user expectations. No contradiction with 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?
Three sentences, front-loaded with the core purpose, and every sentence adds value: what it finds, where to start, and what it returns. The naming examples are illustrative without being verbose.
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 search tool with only two parameters and no output schema, the description covers purpose, scoping context, and return categories well. It does not detail result shape or empty-result behavior, but those are not critical for selecting and invoking 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?
The description reinforces that 'query' is a subject to search for, but the schema already covers that with examples. The 'limit' parameter is not described in the description, and with only 50% schema description coverage, the description does not fully compensate. The parameter is simple enough that the gap is moderate rather than severe.
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 ('Find') and resource ('resource, tool or guide for a subject') and clearly distinguishes this as the starting point for discovery. It explains the tool's role relative to siblings by highlighting Productive's naming conventions, so an agent knows this is a search/capability lookup, not a data operation.
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?
'Start here' explicitly tells the agent when to use this tool, and the mention of Productive's non-obvious naming ('a budget is a deal') explains why guessing would waste calls. It does not name alternative sibling tools or state when not to use it, but the contextual guidance is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Log a time entry without looking up what Productive insists on. Time is stored in MINUTES — pass hours and it converts. The service (the budget line the work bills to, not the task) is resolved from the task when not given, and the person is resolved from the authenticated caller, so time lands on the right person even though the server holds one shared token.
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | Date of the work, YYYY-MM-DD. | |
| note | No | ||
| hours | No | Time worked in hours; converted to minutes. | |
| taskId | No | Task worked on. Also used to resolve the service. | |
| minutes | No | Time worked, in minutes. | |
| personId | No | Whose time this is. Defaults to the resolved caller. | |
| serviceId | No | Service (budget line) to bill to; overrides the task lookup. | |
| billableMinutes | No | Billable portion, if it differs from the time worked. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With only readOnlyHint=false in annotations, the description carries the full burden of behavioral disclosure. It clearly reveals that hours are converted to minutes, the service is derived from the task when absent, and the person is resolved from the authenticated caller despite a shared server token. This is specific and valuable transparency.
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?
Three sentences, each earning its place: the first states the action, the second explains the critical unit conversion, and the third clarifies lookup defaults and authentication behavior. It is dense without being bloated, and the essential action is front-loaded.
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 an 8-parameter tool with no output schema, the description covers the core call flow well: what to log, how units are handled, which IDs can be omitted, and how resolution works. It does not describe the return value or what happens when conflicting inputs like hours and minutes are both supplied, but these are minor gaps given how much is already explained.
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 88%, so the schema already documents most parameters. The description adds real value by explaining the hours-to-minutes conversion, the distinction between service and task, and the fallback resolution behavior for serviceId and personId. It does not explain every edge case, such as providing both hours and minutes, but it meaningfully supplements 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 opens with a specific verb and resource: 'Log a time entry.' It goes beyond the title by explaining the key behavior that makes this tool distinct from generic create/list tools: it avoids manual lookups and handles conversions, making the intent unmistakable.
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 gives clear practical context: service can be omitted and is resolved from the task, person can be omitted and is resolved from the caller. This tells an agent when it can avoid passing certain parameters, though it does not explicitly name alternatives or state when not to use the tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Update a record. PATCH semantics, verified live: attributes you omit keep their values, so there is no need to resend the whole record. On a financial or administrative resource this returns a staged operation instead of writing.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| resource | Yes | ||
| attributes | No | Attributes to write, by their Productive names. Validated against the resource contract first, because Productive answers 200 and ignores an unknown attribute rather than reporting it. Custom-field values go in a nested `custom_fields` object keyed by field id. | |
| relationships | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With only readOnlyHint=false provided by annotations, the description carries a meaningful behavioral burden and does well: it discloses PATCH semantics, confirms omitted fields are retained, and warns that certain resources return a staged operation instead of writing. It does not cover permissions or reversibility, but it adds real behavioral facts beyond 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?
The description is short and front-loaded with the key PATCH behavior. The first sentence duplicates the title, which costs a little, but every other sentence carries useful operational meaning without padding.
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 description covers core PATCH semantics and the staged-operation caveat, but it does not explain what a staged operation should be followed by, especially given the sibling productive_commit_operation. There is also no output schema and the description does not say what a normal update returns, and relationships remain undocumented. This leaves the agent needing to infer important follow-up 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?
Schema coverage is low at 25%, and the description does add value for the attributes parameter by explaining that omitted attributes keep their values. However, it gives no semantic guidance for resource, id, or relationships, and the custom_fields nesting behavior lives only in the schema, not 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 states a clear action on a record and immediately clarifies PATCH semantics, which distinguishes it from read-only siblings like productive_get and productive_list. It is slightly repetitive with the title but unambiguous about the operation's 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 gives useful context: partial updates preserve omitted attributes, so no full resend is needed, and financial/admin resources return staged operations instead of writing. However, it does not explicitly say when to choose this over productive_create, productive_delete, or productive_run_action, so usage versus alternatives is mostly implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.2.0productive_check_connectionproductive_commit_operationproductive_createproductive_deleteproductive_describe_custom_fieldsproductive_describe_resourceproductive_getproductive_listproductive_run_actionproductive_search_capabilitiesproductive_track_timeproductive_updateScored across 12 tools
Each tool targets a distinct operation: reading one record, listing, creating, updating, deleting, running a named action, staging/committing writes, tracking time, or performing a meta/introspection task. Even the closer pairs like get/list and describe_resource/describe_custom_fields are clearly separated by resource scope and purpose.
All tools follow the snake_case productive_<verb> pattern, with most using verb_noun (productive_check_connection, productive_run_action) and a few using bare verbs (productive_get, productive_list). This is consistent and predictable, with no style mixing or vague duplicate verbs.
Twelve tools is well within the ideal scope for a broad API integration server. The set covers generic CRUD, named actions, time tracking, staged writes, and meta/discovery tools without ballooning into redundant or overly granular endpoints.
The server provides full lifecycle coverage: read, list, create, update, delete, run actions, and commit staged operations, plus support for custom fields and resource contracts. The meta tools fill the gaps that typically cause agent failures, such as knowing resource-specific filters, writable attributes, and custom-field keying.
Agent-complete, permission-scoped product operations for Priorify workspaces.
Supervised API-write gateway for AI agents with policy, human approval and execution receipts.
Read time entries, projects, clients, tasks and invoices; log and update tracked time.
Read teams, spaces, lists and tasks; create, update and comment on tasks and track time.