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dns-doctor

by dev.dnsdoctor

check_propagation

Read-onlyIdempotent

Check whether a DNS change has propagated GLOBALLY: six vantage points (five owner-run probes on separate continents plus this server's own resolver) each read the same name through several resolvers, and the grid plus a deterministic verdict comes back. Call it after the human publishes a record — you have ONE network vantage point, and a record that resolves for you can still be missing elsewhere. name is the exact name (www. is not stripped, _dmarc.example.com works), record_type is A|AAAA|CNAME|MX|TXT|NS, and the optional expected_value turns each cell into match or mismatch instead of agreement-only. Observation only: no record is ever composed here. A cell that did not answer is unavailable, which is NOT a negative result, and when fewer than three vantage points were reached the verdict downgrades to unknown — report vantage_reached of vantage_total rather than calling a name converged on partial coverage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe exact DNS name to look up, e.g. example.com, www.example.com or _dmarc.example.com. It is used as given — a leading www. is NOT stripped and underscore labels are kept — so pass the name the record is actually published at, not the registrable domain.
record_typeNoThe record type to read at that exact name (default A). SPF and DMARC records are TXT — pass TXT with the right name rather than expecting a derived query name.A
expected_valueNoOptional value the record should now hold, e.g. '1.2.3.4' or the new DMARC record text. Supply it and each cell is reported as match or mismatch against it; omit it and the check only reports whether the vantage points agree with each other.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the readOnly/destructive hints, the description adds valuable behavioral detail: it is observation-only, cells can be 'unavailable' without being negative, verdicts downgrade to 'unknown' below three vantage points, and partial coverage should be reported honestly. 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?

The description is dense but every sentence earns its place: purpose, usage trigger, parameter semantics, edge-case handling, and reporting guidance. It is front-loaded with the core question and then adds necessary caveats.

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 complexity and the presence of an output schema, the description is complete. It covers when to call the tool, how params affect behavior, how to interpret non-answers, and how to report degraded results, leaving nothing essential for an agent to infer.

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 coverage is 100%, so the baseline is 3. The description restates key parameter facts (exact name, record_type values, expected_value behavior) but does not add significant new meaning beyond what the input schema already provides.

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 and resource: check whether a DNS change has propagated globally across six vantage points. It explains the concrete output (grid plus deterministic verdict) and the role of the tool well enough to distinguish it from sibling record-checking tools like check_record or check_dkim_selector.

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?

It explicitly says to call it after a human publishes a record and explains why a single network vantage point is insufficient. It does not explicitly name sibling alternatives or exclusion conditions, but the guidance is clear and actionable.

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

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TDQS

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct DNS or email authentication task. Despite multiple DMARC-related tools, their purposes are clearly differentiated: build_dmarc_upgrade upgrades policies, generate_dmarc_record creates new ones, validate_dmarc_record validates pasted records, and scan_domain vs get_report differentiate fresh vs cached scans.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., build_dmarc_upgrade, check_record, scan_domain). No mixing of naming conventions, making the set predictable and easy to navigate.

Tool Count5/5

With 11 tools, the server is well-scoped for a DNS/email authentication diagnostics tool. The number covers essential operations without being overwhelming or sparse.

Completeness4/5

The tool set covers core workflows: DMARC management (generate, validate, upgrade, parse reports), DNS checks (propagation, reverse DNS, SPF lookups), and DKIM selector checks. Minor gaps exist, such as the absence of SPF validation or DKIM signature verification, but the main diagnostic and monitoring use cases are supported.

Resources