Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@seismic-mcp compare earthquake reports from USGS and JMA for the last 24 hours"
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.
A unified Model Context Protocol server for global earthquake data. Queries 20 seismic agencies in parallel; matches cross-agency reports of the same earthquake; surfaces magnitude and location discrepancies so AI agents can see what one feed alone would hide.
Working:
20 agencies wired up. FDSN-event: USGS, EMSC, IRIS, INGV, GeoNet, GFZ, NOA, IPGP, NCEDC, SCEDC, ISC, SED, BMKG, NIEP, RESIF, KNMI, NRCAN. Custom (non-FDSN): JMA (Japan), AFAD (Türkiye), IMO (Iceland).
Cross-agency reconciliation. Spatiotemporal clustering, prime-report selection (local authority > EMSC for Europe > USGS), discrepancy detection (magnitude/location/depth spread). Reviewed bulletins trump preliminary.
Seven MCP tools: find_events, get_event, compare_sources, find_discrepancies, list_recent_by_agency, get_agency_info, list_agencies.
TTL cache (60 s) with in-flight de-dup: repeated queries are sub-100 ms.
57 unit tests covering parsers, matching, prime-selection, authorities, reconciliation, and cache.
HTTP frontend for manual inspection: form + Leaflet map + side-by-side compare drawer.
Not yet built: EMSC eventid mapping (cross-references USGS/EMSC IDs for tighter matching), populated known_aliases.json, multi-region per-event authority (KOERI alongside AFAD for Türkiye).
Related MCP server: wems-mcp-server
Reports earthquake data from multiple seismic agencies for research, journalism, situational awareness, and curiosity. It is NOT an early-warning system. All earthquake reports arrive AFTER shaking has already occurred where it was felt. For earthquake preparedness and emergency response, consult official local authorities (USGS ShakeAlert, JMA Earthquake Early Warning, etc.). Preliminary magnitudes and locations are routinely revised by reporting agencies; never make safety decisions based on a single reading.
Once published to PyPI, no checkout is needed — uvx fetches and runs it:
uvx seismic-mcpAdd to claude_desktop_config.json — Windows path is %APPDATA%\Claude\claude_desktop_config.json, macOS ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"seismic": {
"command": "uvx",
"args": ["seismic-mcp"]
}
}
}To run from a local checkout instead (development):
uv sync
uv run python server.pyRestart Claude Desktop. The tools appear under the seismic-mcp server in the tools menu.
uv run python web.pyThen open http://127.0.0.1:8765/. Same code paths as the MCP server, just exposed over HTTP with a small UI.
uv run pytestserver.py FastMCP entry point — 7 @mcp.tool functions
web.py FastAPI HTTP wrapper around the same tools
src/
schemas.py Pydantic models: SourceReport, ReconciledEvent, ...
adapters/
base.py SeismicAdapter protocol
fdsn.py Header-aware FDSN-event adapter (handles 17 networks)
afad.py Custom AFAD JSON adapter
jma.py Custom JMA list.json adapter
imo.py Custom IMO GeoJSON adapter
__init__.py Registry: make_adapter(code), supported_agencies()
authorities.py Regional-authority bbox lookup
matching.py Spatiotemporal clustering with mag-scaled thresholds
prime_selection.py Headline-report selection rules
reconcile.py Cluster → ReconciledEvent with spreads
cache.py TTL cache + in-flight de-dup
tools/ One file per MCP tool
data/
agency_metadata.json Per-agency coverage, latency, notes
region_authorities.json Region bboxes → authoritative agency
known_aliases.json (manual fallback for matching, currently {})
tests/ pytest suite — 57 tests, fast (<1s)
static/index.html Single-page UI for the HTTP frontendSide-by-side per-field comparison of every agency's report for one event.
Returns a field-major view (magnitude, lat, lon, depth, time, ...) with one column per agency and the numeric spread per field. Use this when an agent needs to explain why sources disagree on a specific earthquake.
| Name | Required | Description | Default |
|---|---|---|---|
| canonical_id | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the disclosure burden. It details the output structure (field-major view, columns per agency, numeric spread), which goes beyond the name. It does not mention side effects or permissions, but for a comparison tool the absence of mutation claims is acceptable given the query-like purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences with no filler. The first sentence delivers the action and output format; the second provides the usage condition. The most important information 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?
Given the tool's simplicity (one input parameter), the existence of an output schema, and the description that explains both the input's meaning and the output's structure, nothing critical is missing. It fully equips an agent to understand when and how to call it.
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 the single parameter canonical_id. Although it never explicitly defines the parameter, the phrases 'for one event' and 'a specific earthquake' clearly imply that canonical_id identifies the event. This is sufficient for a single, obvious 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?
Description clearly states a specific verb ('compare'), resource ('agency reports for one event'), and the output (field-major view with per-agency columns and numeric spread). It distinguishes itself from siblings like find_discrepancies by focusing on explaining disagreements, not just listing them.
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?
Explicitly says 'Use this when an agent needs to explain *why* sources disagree on a specific earthquake', providing a clear context. However, it does not mention alternative tools or when NOT to use it, so it earns a 4 rather than a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return events where agency reports disagree past the given thresholds.
Same query params as find_events, plus tunable spread thresholds. By
default keeps only events reported by 2+ agencies, since a lone report
cannot disagree with itself. Useful for journalism, QA of preliminary
feeds, or surfacing where the global picture is genuinely uncertain.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| agencies | No | ||
| end_time | No | ||
| radius_km | No | ||
| center_lat | No | ||
| center_lon | No | ||
| start_time | No | ||
| max_latitude | No | ||
| min_latitude | No | ||
| max_longitude | No | ||
| max_magnitude | No | ||
| min_longitude | No | ||
| min_magnitude | No | ||
| min_magnitude_spread | No | ||
| require_multi_agency | No | ||
| min_location_spread_km | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It explains a non-obvious default—'keeps only events reported by 2+ agencies, since a lone report cannot disagree with itself'—which is genuinely useful. The read-only nature is implied by 'Return events,' and the output schema covers return details, so the description does not need to repeat them.
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: purpose, parameter relationship to find_events, then default behavior and use cases. The most identifying information is front-loaded, and every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 16 parameters, no annotations, and an output schema, the description is largely complete: it explains what the tool returns, how it relates to find_events, its default filtering behavior, and when it is useful. The main minor gap is the lack of explicit contrast with sibling tools such as compare_sources.
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 compensates only partially by saying 'Same query params as find_events, plus tunable spread thresholds.' This tells the agent which parameters are inherited and which category the extra ones fall into, but it does not enumerate or define the threshold parameters; the property names like min_magnitude_spread and min_location_spread_km carry much of the semantic load.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The opening sentence, 'Return events where agency reports disagree past the given thresholds,' is a specific verb+object statement that makes the tool's core function unmistakable. It also distinguishes itself from find_events by noting it adds 'tunable spread thresholds,' so an agent can tell this is the discordance-focused variant of the event search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: 'Useful for journalism, QA of preliminary feeds, or surfacing where the global picture is genuinely uncertain.' It also references find_events for shared query parameters, which orients the agent. However, it does not explicitly state when not to use it or compare it with alternatives like compare_sources.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find recent or historical earthquakes, reconciled across multiple seismic agencies.
Queries every supported agency in parallel (~20 networks including USGS, EMSC, JMA, INGV, GeoNet, AFAD, and others) and clusters their reports so each returned event lists every agency that picked it up — the agent sees cross-network disagreement explicitly when it exists. Unreachable agencies fail gracefully and are skipped; a 60s TTL cache makes repeat queries cheap.
Pass agencies=["USGS", "EMSC", ...] to restrict the source set. Use
list_agencies to see all supported codes.
NOT for emergency or early-warning use; data is preliminary.
Defaults: last 24 hours, no magnitude filter, limit 50.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| agencies | No | ||
| end_time | No | ||
| radius_km | No | ||
| center_lat | No | ||
| center_lon | No | ||
| start_time | No | ||
| max_latitude | No | ||
| min_latitude | No | ||
| max_longitude | No | ||
| max_magnitude | No | ||
| min_longitude | No | ||
| min_magnitude | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the disclosure burden and does so thoroughly: parallel queries across ~20 agencies, graceful handling of unreachable agencies, a 60s TTL cache, explicit cross-network disagreement, preliminary-data caveat, and default behavior. No side-effect or contradiction concerns.
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?
Every sentence earns its place: purpose first, behavior, usage hint, safety caveat, then defaults. Four short paragraphs are easy to scan and contain no filler.
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 13-parameter tool with an output schema, the description covers what matters for invocation: source restriction, defaults, cache behavior, failure mode, preliminary nature, and a pointer to list_agencies. Remaining filter details are self-explanatory from parameter names and schema types.
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 burden falls on the description. It explains agencies and the effective time/magnitude/limit defaults, which adds real value, but the other 11 parameters (center/radius, bounding box, magnitude ranges) are left to their names; interactions and constraints between filter families are not specified.
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 ('Find recent or historical earthquakes') and adds the defining trait 'reconciled across multiple seismic agencies.' It explains each returned event lists every agency that picked it up, which separates it from singular get_event and agency-specific list_recent_by_agency.
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 gives concrete usage context: pass agencies to restrict sources, use list_agencies for supported codes, and warns against emergency use. It does not explicitly contrast with compare_sources or find_discrepancies, so it stops short of full when-to-use versus alternatives guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Static metadata about a seismic agency: coverage, latency, magnitude completeness, notes.
Use to interpret cross-agency disagreement (e.g. JMA magnitudes are systematically lower than USGS Mw for the same event) or to decide which agency is authoritative for a region.
| Name | Required | Description | Default |
|---|---|---|---|
| agency | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. 'Static metadata' clearly implies a non-mutating, read-only lookup, and the listed fields set expectations for what kind of information is returned. It does not discuss access requirements or behavior for unknown agencies, but those are secondary for a metadata lookup.
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 compact sentences with no filler. The core metadata content is front-loaded, and the second sentence earns its place by explaining practical use cases for the tool.
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 static lookup with an output schema, the description covers the resource, the returned fields, and when to use it. The main gap is the lack of explicit accepted value format for the agency parameter, but the examples partially compensate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides only 'agency' as a string with no description (0% schema coverage). The description adds meaning by framing it as a seismic agency and giving real examples like JMA and USGS, which imply codes/abbreviations, but it never explicitly states whether a code, full name, or ID is expected.
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 first sentence clearly defines the resource: static metadata about a seismic agency, listing specific fields (coverage, latency, magnitude completeness, notes). It is clearly about agency details rather than events or comparisons, but it does not explicitly differentiate itself from siblings like list_agencies or compare_sources.
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 explicit when-to-use context: interpret cross-agency disagreement or decide which agency is authoritative. However, it does not mention when not to use this tool or point to alternatives such as compare_sources or list_agencies.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch one reconciled event by canonical_id (format: 'AGENCY:event_id').
Re-queries the seed agency by ID, then sweeps every supported agency in parallel within ±10 minutes and ~3° of the seed to find matching reports. Returns the reconciled cluster, or None if the seed cannot be found.
Use canonical_ids from find_events (e.g. 'USGS:us6000sze1') to drill
into a single event with the full cross-agency view.
| Name | Required | Description | Default |
|---|---|---|---|
| canonical_id | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations supplied, the description carries the full burden and does so admirably. It reveals the internal behavior: re-querying the seed agency, sweeping all supported agencies in parallel within ±10 minutes and ~3°, and returning None when the seed cannot be found. This is unusually specific and useful.
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 compact and well-organized: a one-sentence purpose with format, a behavioral paragraph explaining the matching logic, and a usage paragraph with an example. No sentence is filler; each adds distinct value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists, the return value shape does not need elaboration. The description covers the input format, the search behavior, the failure condition, and the relationship to find_events. This is complete enough for reliable 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 only defines canonical_id as a string with 0% description coverage, so the description must compensate. It fully does: it specifies the exact 'AGENCY:event_id' format, gives the example 'USGS:us6000sze1', and tells the agent to source the value from find_events. This removes all ambiguity about the required 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 opens with a specific verb and resource: 'Fetch one reconciled event by canonical_id'. It also distinguishes itself from find_events by positioning get_event as the drill-down counterpart that provides a 'full cross-agency view'. This clearly separates it from the sibling list/comparison 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?
It explicitly instructs the agent to use canonical_ids from find_events, provides a concrete example, and states that this tool is for drilling into a single event. It does not enumerate exclusions or compare against alternatives like compare_sources, but the guidance is clear enough for correct selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
All agency codes this server knows about (whether FDSN-wired or not).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It states the scope accurately, including the non-FDSN-wired behavior, but does not explicitly state that this is a read-only, side-effect-free operation or describe ordering/pagination. The behavior is largely inferable from the word 'list', but not fully explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no filler. It front-loads the core purpose and adds one meaningful qualifier about FDSN-wired inclusion, which is exactly the kind of information an agent needs without any waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity, zero parameters, and existing output schema, the description is sufficient for an agent to invoke it correctly. The only additional context that might help would be usage guidance, but the simplicity of this list operation makes that a minor omission.
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 schema is empty, so there is no parameter semantics for the description to clarify. The baseline for a zero-parameter tool is 4, and the description does not introduce any unnecessary parameter-related content.
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 identifies the resource ('all agency codes') and the action (listing them). The parenthetical '(whether FDSN-wired or not)' adds a valuable scope distinction that separates this from any FDSN-only agency list or agency-specific tools like get_agency_info.
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 when to use the tool: when you need the complete set of agency codes known to the server. However, it does not explicitly mention alternatives or exclusions, so an agent could still wonder whether get_agency_info or list_recent_by_agency would be more appropriate for a given request.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Raw recent events from a single agency, without cross-agency reconciliation.
Returns SourceReport objects exactly as the agency reported them. Use
this to see what one network alone is publishing (often includes small
local events the global feeds miss) or to compare against the reconciled
view from find_events.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| agency | Yes | ||
| end_time | No | ||
| start_time | No | ||
| min_magnitude | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden, and it discloses meaningful behavior: results are raw, single-agency, un-reconciled, and returned 'exactly as the agency reported them.' It could add ordering or time-window details, but core behavioral fidelity is clear.
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 dense, front-loaded sentences with no fluff. The core behavior is in the first phrase, and usage context follows without repetition.
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 purpose and distinguishes from `find_events`, and an output schema covers return structure. But with no annotations and 0% parameter coverage, useful details like the default recent window, ordering, and `min_magnitude` behavior are missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description does not compensate. It only implies agency and recency; it does not explain `limit`, `start_time`, `end_time`, or `min_magnitude` 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?
The description uses a specific verb-resource pairing ('list recent events from a single agency') and immediately distinguishes this tool from the reconciled sibling by saying 'without cross-agency reconciliation.' It also names `find_events` as the reconciled view, so an agent can tell them apart.
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 tells the agent when to use this tool: to see what one network alone is publishing, especially small local events, and to compare against `find_events`. The alternative is named, and the condition distinguishing them is clear.
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.
v0.1.0compare_sourcesfind_discrepanciesfind_eventsget_agency_infoget_eventlist_agencieslist_recent_by_agencyScored across 7 tools
Each tool targets a distinct operation: finding reconciled events, retrieving a specific event, comparing source reports, surfacing disagreements, listing agencies, listing raw agency feeds, and fetching agency metadata. The overlap between find_events and find_discrepancies is clearly delineated by threshold filtering, and list_recent_by_agency is explicitly positioned as the raw single-agency counterpart to the reconciled find_events.
All tools follow a snake_case verb_noun pattern: find_events, get_event, compare_sources, find_discrepancies, list_agencies, list_recent_by_agency, get_agency_info. The verbs are varied but predictable (find/list/get/compare), and the noun usage (plural for collection-style operations, singular for single-item fetches) is consistent.
Seven tools is well within the ideal 3-15 range and each tool earns its place in the cross-agency seismic data domain. The count is lean enough to avoid overwhelming an agent while covering the core workflows of discovery, retrieval, comparison, and metadata lookup.
The domain is a read-only, cross-agency earthquake data service, and the surface is complete: discover reconciled events, fetch a specific event, compare raw source fields, find disagreement hotspots, enumerate agencies, inspect a single agency's raw feed, and understand agency-specific biases. No obvious dead ends exist—every tool's output either feeds into another tool or stands alone as a meaningful answer.
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