How do I use tessera-mcp?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@tessera-mcp check Atlas Freight for double billing" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

tessera-mcp

by NDilanka

Overview Schema Source Related Servers Discussions Admin

TypeScript

Hybrid

tessera-mcp

A Model Context Protocol (MCP) server that gives an AI agent seven support-ops tools for Tessera, a fictional B2B workspace SaaS. It runs over stdio with a bundled SQLite database — no hosted runtime, no API keys, no LLM calls anywhere in this repo.

Why this exists (60-second pitch). Wiring an LLM to tools is easy; wiring it safely is the hard part. This server demonstrates the pattern I ship for agentic work: typed tools with LLM-readable descriptions, a dry-run/confirm human-in-the-loop gate on every mutation, and a deterministic test suite that proves the tools behave — 20/20, no model in the loop. Point Claude Code at it and watch it run a realistic morning ops workflow: spot a double-billed customer, investigate, refund with approval, rescue a churning trial.

Quickstart

# 1. Create + seed the local database (idempotent — safe to re-run)
npx tessera-mcp --seed

# 2. Register the server with Claude Code
claude mcp add tessera -- npx tessera-mcp

That's it. Claude Code now has the seven tools below. No environment variables required — the server uses a local SQLite file at ~/.tessera-mcp/data.db. The path is home-anchored (not relative to the current directory) so the server finds the same database no matter where the host launches it from, and it auto-seeds on first run if you skip step 1.

Requires Node 20+.

Claude Desktop

Add this to your claude_desktop_config.json (Settings → Developer → Edit Config):

{
  "mcpServers": {
    "tessera": { "command": "npx", "args": ["tessera-mcp"] }
  }
}

Claude Desktop launches MCP servers from the filesystem root, so a working-directory-relative database would be empty. This server sidesteps that by using the home-anchored ~/.tessera-mcp/data.db and seeding it on first run — no --seed step required.

Related MCP server: SecureAgentServer

The tools

Tool	Kind	What it does
`list_open_tickets({ priority?, limit? })`	read	Open tickets, worst priority first; optional priority filter + limit
`get_customer({ idOrEmail })`	read	One customer by id or email, with their invoices + tickets summary
`search_invoices({ customerId?, status?, minAmount? })`	read	Filter invoices — spot duplicates, overdue balances, history
`daily_summary()`	read	Start-of-day snapshot: open tickets by priority, overdue invoices ($), trials expiring ≤7d
`update_ticket_status({ ticketId, status, confirm? })`	mutation	Move a ticket to open / pending / solved
`extend_trial({ customerId, days, confirm? })`	mutation	Push a trial customer's end date out by N days
`issue_refund({ invoiceId, reason, confirm? })`	mutation	Refund a paid invoice (rejects already-refunded / non-paid)

Every input field carries a description — that text is what the client LLM reads to call the tool correctly. Results come back as concise text tables, not JSON dumps. Invalid ids return clear error strings, never exceptions.

The confirm / dry-run pattern (human-in-the-loop)

MCP has no built-in "approve this action" UI — the protocol hands a tool call straight to your code. So every mutation here is gated by a confirm flag that defaults to false:

Without confirm: true the tool changes nothing and returns a preview:

DRY RUN — would refund INV-1013 ($2,400.00, Atlas Freight) — reason: "double billed".
Re-call with confirm:true to execute.

With confirm: true it executes atomically (a single write batch that also appends an audit-log row) and reports what changed:
```
Done — refunded INV-1013 ($2,400.00, Atlas Freight). Reason: "double billed".
```

The agent shows the human the dry-run preview, waits for a yes, then re-calls with confirm:true. It turns an irreversible action into a two-step, reviewable one. See DECISIONS.md for why this beats an ungated tool.

Example Claude Code session (illustrative)

Transcript below is an illustration of the intended workflow, lightly trimmed. The deterministic suite (npm run eval) is the actual correctness metric.

You:  Good morning — anything on fire?

Claude:  [calls daily_summary]
  Open tickets: 10 (3 urgent). 4 overdue invoices ($2,847.00). 2 trials expiring within 7 days.
  One thing stands out: Atlas Freight has two identical $2,400 invoices issued the same day.

You:  Dig into Atlas Freight.

Claude:  [calls get_customer({ idOrEmail: "CU-1010" })]
  Atlas Freight (CU-1010), enterprise. INV-1013 and INV-1014 are both $2,400.00,
  both issued Jul 14, 2026 — that's a double charge.

You:  Refund one of them.

Claude:  [calls issue_refund({ invoiceId: "INV-1013", reason: "duplicate charge — double billed" })]
  DRY RUN — would refund INV-1013 ($2,400.00, Atlas Freight). Re-call with confirm:true to execute.
  Want me to go ahead?

You:  Yes.

Claude:  [calls issue_refund({ ..., confirm: true })]
  Done — refunded INV-1013 ($2,400.00, Atlas Freight).

You:  Also, Paperkite's trial is about to lapse and they're angry — give them two more weeks and close their ticket.

Claude:  [extend_trial CU-1003 +14 (dry-run → confirm), then update_ticket_status TK-1003 solved (dry-run → confirm)]
  Done — Paperkite's trial now ends Aug 3, 2026, and TK-1003 is solved.

You:  Re-run the summary.

Claude:  [calls daily_summary]
  Open tickets down to 9 (2 urgent). Overdue unchanged. Trials expiring within 7 days: down to 1.

Eval — the honest metric

npm run eval connects an in-process MCP client to the server over the SDK's linked in-memory transport (no child process, no network, no LLM) and replays 23 scripted cases from data/eval-cases.json against a freshly seeded, isolated database:

valid reads, filtered reads, and invalid-id errors
each mutation as dry-run and then confirmed
post-mutation state assertions (a re-read proves the change landed)
a same-status no-op that returns success, not an error
direct audit_log assertions (exactly one row per confirmed mutation)
refund-twice rejection (a second refund on an already-refunded invoice errors)
a concurrency check: two overlapping confirmed refunds race via Promise.all; exactly one wins and exactly one audit row is written (proving the TOCTOU fix)

The eval is isolated — it runs against its own file:eval.db and never inherits TURSO_DATABASE_URL, so npm run eval can never touch your real or hosted data.

Because there is no model in the loop, the suite is fully deterministic: the gate is 100%. Anything less is a real bug and the process exits non-zero.

23/23 cases passed.
All 23 eval cases + concurrency check passed (deterministic gate: 100%).

Configuration

Zero config by default. Optional environment variables:

Variable	Effect
`TURSO_DATABASE_URL`	Use a hosted libSQL/Turso database instead of the default `~/.tessera-mcp/data.db`
`TURSO_AUTH_TOKEN`	Auth token for the hosted database above
`TESSERA_NOW`	Override the demo's fixed reference clock (ISO 8601); defaults to `2026-07-16T12:00:00Z` so the eval stays deterministic. An invalid value is ignored with a stderr warning
`EVAL_DB_URL`	Database the eval uses (default `file:eval.db`). The eval ignores `TURSO_DATABASE_URL` entirely so it can never wipe real data
`EVAL_ALLOW_REMOTE`	Set to `1` to allow the eval to target a remote (non-`file:`) URL; otherwise remote eval URLs are refused

CLI flags: --seed (create + seed, then exit), --db <path> (use a specific SQLite file), --help. The default database is ~/.tessera-mcp/data.db, auto-seeded on first run.

See env.example. This project never reads or writes .env files.

The Tessera universe

Tessera is a fictional B2B workspace SaaS used across a family of portfolio demos. Sibling repos:

docs-chat — RAG over Tessera's docs
tessera-ops-agent — the ops dashboard + copilot this data is modeled on
tessera-invoice-inbox — invoice-processing workflow

All data here is synthetic. See DECISIONS.md for architecture rationale and VIDEO-SCRIPT.md for the demo narration.

License

MIT

Available Tools

7 tools

daily_summaryDaily summaryA

Read-only

A start-of-day operations snapshot: open tickets broken down by priority, count and dollar total of overdue invoices, and how many trials expire within 7 days. Takes no arguments.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A4.5/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, and the description adds valuable context by enumerating exactly what data the snapshot covers. It avoids contradicting the read-only nature and provides a precise scope of information returned.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that front-loads the purpose and then lists key contents. Every word earns its place, with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-argument, read-only tool without an output schema, the description fully covers the tool's purpose and the data it provides. The level of detail is appropriate for an AI agent to decide when to invoke it and what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With zero parameters and schema coverage at 100%, the description's statement 'Takes no arguments' reinforces the schema. No parameter explanation is needed, and the baseline of 4 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides a start-of-day snapshot with specific components (open tickets by priority, overdue invoice totals, trials expiring within 7 days). This distinguishes it from sibling tools like list_open_tickets and search_invoices by aggregating multiple data sources into a summary.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'start-of-day operations snapshot' provides clear context for when to use this tool. It implies use as a daily overview rather than for detailed list views, though it does not explicitly exclude alternatives or mention when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

extend_trialExtend trialA

Extend a trial customer's trial end date by a number of days. Only works for customers whose status is 'trial'. Dry-run by default: returns a preview unless confirm:true is passed.

ParametersJSON Schema

Name	Required	Description
`days`	Yes	Number of days to add to the current trial end date (1-90).
`confirm`	No	Safety gate. Omit or false for a DRY RUN that changes nothing and returns a preview of what would happen. Pass true only after the human has approved, to actually execute the change.
`customerId`	Yes	The trial customer's id, like CU-1003.

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotations are sparse (all false), so the description carries the burden of explaining behavior. It adds crucial information: the tool is dry-run by default and only actually changes data when confirm:true is passed. It also discloses the status restriction. This adds value beyond the annotations and does not contradict them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is exactly two sentences, front-loaded with the core action. The second sentence packs the critical constraints and safety behavior. There is no filler or redundancy, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 3 parameters, no output schema, and only basic annotations, the description covers the essential aspects: purpose, precondition, and dry-run default. A minor gap is not describing what happens if the status is not trial, but 'only works for' is sufficient. Overall, it is complete enough for an agent to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Every parameter in the schema has a thorough description, including the confirm safety gate. The tool description does not add additional parameter semantics beyond what the schema already provides. With 100% schema coverage, a baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

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: 'Extend a trial customer's trial end date by a number of days.' It adds a clear constraint ('Only works for customers whose status is 'trial'') that differentiates it from sibling tools like get_customer or update_ticket_status. This is a strong, unambiguous purpose statement.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear usage context: it only applies to trial-status customers, and it implies a safe workflow by explaining the dry-run default. It does not explicitly name alternatives or exclusions, but the precondition and safety gate offer sufficient guidance. A score of 4 reflects clear context without explicit sibling comparisons.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_customerGet customerA

Read-only

Look up one customer by id or email and return their profile plus a summary of their invoices and tickets. Use this to investigate a specific account.

ParametersJSON Schema

Name	Required	Description	Default
`idOrEmail`	Yes	A customer id like CU-1007, or the customer's email address like grace@meridian.hk.

TDQS

A4.3/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds value beyond the readOnlyHint annotation by explaining that the tool aggregates a customer's profile with invoice and ticket summaries. This clarifies the scope and return content without contradicting the read-only nature.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the key action and outcome, and contains no superfluous information. Every word adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter, read-only lookup tool with no output schema, the description sufficiently explains what the tool returns (profile plus invoice/ticket summaries) and when to use it. The simplicity and clear annotations make this complete without needing more detail.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already provides full coverage (100%) with a detailed description of idOrEmail including examples. The description's mention of 'by id or email' aligns with the schema but adds no additional semantic detail beyond what is already structured.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool looks up a single customer by id or email and returns their profile along with invoices and tickets summaries. This specific verb+resource+scope distinguishes it from sibling tools that list or search across multiple records.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use this to investigate a specific account,' providing clear when-to-use guidance. It does not explicitly list alternatives or when not to use it, but the focus on a single customer implies broader list/search tools are for different scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

issue_refundIssue refundA

Destructive

Refund a paid invoice. Fails if the invoice is not in 'paid' status (e.g. already refunded). Dry-run by default: returns a preview with the amount and customer unless confirm:true is passed.

ParametersJSON Schema

Name	Required	Description
`reason`	Yes	Short human-readable reason for the refund, e.g. 'duplicate charge'. Recorded in the audit log.
`confirm`	No	Safety gate. Omit or false for a DRY RUN that changes nothing and returns a preview of what would happen. Pass true only after the human has approved, to actually execute the change.
`invoiceId`	Yes	The invoice id to refund, like INV-1013.

TDQS

A4.4/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description goes beyond the annotations (which only indicate destructive) by disclosing the dry-run default, preview behavior, and the confirm safety gate. It also mentions the failure condition. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the action and then the safety behavior. Every word earns its place, with no redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers the core behavior, failure condition, dry-run preview, and confirm flag. The only gap is that it doesn't describe the return value after a confirmed execution, but this is minor given the tool's simplicity and the absence of an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides 100% coverage with detailed descriptions for all three parameters, including the safety semantics of confirm. The description adds no additional parameter-specific meaning beyond what the schema states, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb+resource: 'Refund a paid invoice.' This clearly distinguishes it from siblings like update_ticket_status and extend_trial. It also adds a key precondition ('Fails if the invoice is not in paid status'), further clarifying its purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear context: only for paid invoices, dry-run by default, and requires confirm:true to execute. It doesn't explicitly enumerate alternatives, but the sibling tools are unrelated, so the usage context is sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_open_ticketsList open ticketsA

Read-only

List support tickets currently in the 'open' state, worst priority first. Optionally filter by priority and cap the number returned.

ParametersJSON Schema

Name	Required	Description	Default
`limit`	No	Maximum number of tickets to return (1-100). Omit for no limit.
`priority`	No	Only return open tickets of this priority: low, normal, high, or urgent. Omit for all priorities.

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, so the safety profile is known. The description adds valuable behavioral context beyond that: it specifies the state filter ('open'), the sort order ('worst priority first'), and the optional cap. It doesn't describe return format or pagination, but for a simple list-with-options tool, this is sufficient additional disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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 ('List support tickets currently in the open state') and tucks optional behaviors at the end. Every word earns its place with no redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with two optional parameters, a readOnlyHint annotation, and no output schema, the description covers the essential 'what' (list open tickets), the sort order, and the optional filters. The schema fills in parameter details. It doesn't describe return fields, but the 'List' verb makes the array return type obvious, and no further behavior is needed for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and both parameters (limit, priority) have detailed descriptions in the schema. The tool description only paraphrases the filter/cap options and adds no new semantics beyond what the schema provides. Baseline 3 is appropriate when the schema carries the full parameter meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'List' with a clearly defined resource 'support tickets' and explicitly scopes to the 'open' state, plus states a sort order ('worst priority first'). This distinguishes it from siblings like update_ticket_status (which mutates) and get_customer (which fetches a single customer).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly conveys when to use the tool: to list open support tickets, with optional priority filtering and limit. It doesn't explicitly name alternatives or exclusion scenarios, but the sibling tool names make the read-only vs mutation distinction obvious. The context is clear with no misleading exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_invoicesSearch invoicesA

Read-only

Search invoices with optional filters. Useful for spotting duplicate charges, overdue balances, or a customer's billing history.

ParametersJSON Schema

Name	Required	Description
`status`	No	Filter by invoice status: paid, open, refunded, or void. Omit for any status.
`minAmount`	No	Minimum invoice amount in whole US dollars (e.g. 500 means $500.00 and up). Omit for any amount.
`customerId`	No	Restrict to a single customer id like CU-1010. Omit to search across all customers.

TDQS

A4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The readOnlyHint annotation already discloses that this is a safe read operation. The description adds some behavioral context through use cases but does not elaborate on return format, pagination, or result limits. With annotations covering the safety profile, a 3 is appropriate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the action and resource, and contains no filler. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is a relatively simple search operation with three optional parameters, all described in the schema, and a readOnly annotation. The description covers purpose and use cases. However, it does not mention return type or result limits, which would make it fully complete. For a simple tool, it is slightly above average.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with each parameter (status, minAmount, customerId) having a clear description. The tool description only refers to 'optional filters' generically, adding no extra semantic detail beyond what the schema already provides. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Search invoices with optional filters,' which clearly states the specific verb and resource. This distinguishes the tool from sibling tools like list_open_tickets and get_customer, which target different entities or actions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides concrete use cases: 'spotting duplicate charges, overdue balances, or a customer's billing history.' This gives clear context for when to use the tool, though it does not explicitly state when not to use it or name alternative tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

update_ticket_statusUpdate ticket statusA

Idempotent

Change a ticket's status (open, pending, or solved). Dry-run by default: returns a preview unless confirm:true is passed.

ParametersJSON Schema

Name	Required	Description
`status`	Yes	The new status to set: open, pending, or solved.
`confirm`	No	Safety gate. Omit or false for a DRY RUN that changes nothing and returns a preview of what would happen. Pass true only after the human has approved, to actually execute the change.
`ticketId`	Yes	The ticket id to update, like TK-1003.

TDQS

A4.5/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description explicitly discloses the dry-run default and the preview return, which is a critical behavioral trait beyond the annotations. It also correctly indicates mutation consistent with readOnlyHint=false. This goes above and beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One concise sentence, front-loaded with the action and resource, then the key dry-run behavior. Every word earns its place with no waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity, the description plus rich schema and annotations fully cover what the agent needs. The dry-run default is the key contextual nuance and is clearly stated. No output schema exists, but the description indicates a preview return.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

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 each parameter and the confirm safety gate in detail. The description does not add significant extra meaning beyond what the schema provides, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Change' and the resource 'ticket's status', listing the allowed values (open, pending, solved). This distinguishes it from siblings like list_open_tickets or issue_refund, which serve different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context on how to use the tool: it changes ticket status and includes a dry-run by default with confirm:true to actually execute. It does not explicitly mention alternatives or when-not-to-use, but the sibling list indicates no overlap, so the guidance is sufficient.

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.

7 tool updatesv0.1.0Aug 11, 2026
- First observeddaily_summary
- First observedextend_trial
- First observedget_customer
- First observedissue_refund
- First observedlist_open_tickets
- First observedsearch_invoices
- First observedupdate_ticket_status

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.3/5.0

Scored 2026-08-11 03:45 across 7 tools

Disambiguation5/5

Each tool targets a distinct resource and action: tickets, customers, invoices, and trial management. No two tools overlap in purpose; daily_summary aggregates but does not duplicate list_open_tickets.

Naming Consistency4/5

Most tools follow a clear verb_noun pattern (list_open_tickets, get_customer, update_ticket_status). The outlier is daily_summary, which uses a noun phrase rather than a verb, creating a minor inconsistency.

Tool Count5/5

With 7 tools, the server is well-scoped for a support and billing domain. Each tool covers a distinct function without bloat or redundancy.

Completeness4/5

The tool set covers ticket status updates, customer lookups, invoice search, refunds, and trial extensions. Minor gaps exist, such as no way to list closed tickets or update customer details, but these are not essential to the core workflow.

Maintenance

ActivityStale

ResponsivenessNo issues

Resources

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