Praesago provides live UK property area intelligence for England & Wales, enabling AI assistants to research, screen, and compare postcode districts using data from over 30 government sources.
Get Area Score: Retrieve the Praesago Trajectory Score (0–100) for any postcode district or full postcode, including national percentile, gentrification stage, and historical calibration.
Get Market Pulse: Access weekly asking-market data: active listings, price reductions, median asking price, and median time on market.
Get Area Context: Full district profile with demographics, income, tenure, deprivation, health, education, broadband, flood risk, council tax, politics, and 12‑month sold prices (API key required for full data).
Search Areas: Screen districts by minimum trajectory score, region, and maximum median price to find up-and-coming areas.
Compare Areas: Side-by-side comparison of two postcode districts on the Trajectory Index.
Search by Place Name: Look up areas by place name, region, or postcode district to find matching areas with scores.
Fetch Area Intelligence: Retrieve full area intelligence for a specific area ID (compatible with ChatGPT deep‑research).
Free tier: 30 calls/day without an API key for basic tools.
Connect via MCP: Use with Claude, ChatGPT, Cursor, or any MCP client via Streamable HTTP or stdio proxy.
UK property area intelligence inside Claude, ChatGPT, Cursor or any MCP client.
Ask your AI assistant "is Walthamstow up and coming?" or "find areas in the North West under £250k with strong fundamentals" and get live, validated answers from Praesago's data — 2,292 England & Wales postcode districts scored monthly from 30+ government data sources (Land Registry, ONS, police.uk, EPC, planning registers and more).
This is a hosted (remote) MCP server — no install, no local process.
https://praesago.com/api/mcpTransport: Streamable HTTP (JSON-RPC 2.0, protocol 2025-03-26).
Tool | What it answers |
| Trajectory Score (0-100), percentile, gentrification stage, backtested calibration |
| Live asking market: active listings, price reductions, median asking, time on market |
| Full district profile: demographics, income, tenure, IMD, health, education, broadband, flood risk, prices |
| Screen districts by score, region and price ceiling |
| Two districts side by side |
| ChatGPT deep-research compatible aliases |
get_market_pulse is collected weekly from property portals and exists
nowhere else: Land Registry reports completions months after the fact, and
the portals themselves do not publish reduction rates or time-on-market.
Related MCP server: UK Property Data
Claude (web / desktop): Settings → Connectors → Add custom connector → paste the URL above.
Claude Code:
claude mcp add --transport http praesago https://praesago.com/api/mcpCursor / other clients: add a remote server with transport http and the
URL above.
Local stdio (any MCP client, or air-gapped configs): this repo ships a zero-dependency stdio proxy to the hosted endpoint, in both runtimes:
node server.js # Node 18+, no npm install needed
python server.py # Python 3.9+, no pip install needed
# or
docker build -t praesago-mcp . && docker run -i praesago-mcpSet PRAESAGO_API_KEY in the environment to unlock full area context.
Tool | Access | What it does |
| Free | Trajectory Score (0–100), national percentile, gentrification early-warning stage and measured historical calibration for any postcode district or full postcode |
| Free | Screen all 2,292 districts by minimum score, region and maximum median price |
| Free | Two districts side by side |
| API key | Full area payload: demographics, income, tenure, deprivation (IMD), health, education, broadband, flood risk, council tax, politics, 12-month prices |
| Free / key | Aliases for ChatGPT deep-research connector compatibility |
Free tier: 30 calls/day, no key needed. Full access: add a
Praesago API key as Authorization: Bearer
or X-API-Key.
The Trajectory Score measures the strength of signals that have historically preceded property price growth. It is a screening aid, not a prediction — and the server's instructions tell your assistant exactly that. Praesago publishes its full validation record, including the windows where the model failed: praesago.com/validation.
Calibration (measured across ~36,700 backtested district-windows): districts scoring 60+ beat their county's median price growth in 63% of three-year windows; districts under 40 managed 37%.
"Is Walthamstow up and coming?"
"Find postcode districts in Greater Manchester under £250k with strong fundamentals"
"Compare E17 and N15 for a buy-to-let"
Methodology: https://praesago.com/methodology
Validation: https://praesago.com/validation
REST API: https://praesago.com/developer
Data sources: HM Land Registry, ONS, police.uk, DfE, Environment Agency, Ofcom, DLUHC and more (Crown copyright / OGL). Coverage: England & Wales.
Compare two UK postcode districts side by side on the Praesago Trajectory Index.
| Name | Required | Description | Default |
|---|---|---|---|
| area_a | Yes | First district, e.g. "E17" | |
| area_b | Yes | Second district, e.g. "N15" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, indicating a safe read operation. The description adds context about comparing 'side by side on the Praesago Trajectory Index', but does not disclose any additional behavioral traits such as whether the comparison is based on a specific metric, how results are presented, or any limitations. With annotations covering safety, the description adds minimal extra 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?
The description is a single concise sentence that front-loads the key action ('Compare two UK postcode districts'). It contains no filler or redundant information, making it efficient for an agent to parse.
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?
Despite having no output schema, the description fails to explain what the comparison returns (e.g., a score, a table, a chart). It only mentions 'side by side on the Praesago Trajectory Index', which is vague. For a tool with two parameters and no output schema, the description should provide more context on the result format to help the agent understand what to expect.
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: both 'area_a' and 'area_b' include descriptions with examples (e.g., 'E17', 'N15'). The description does not add any further meaning beyond what the schema provides, so a baseline score 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 clearly states the tool's purpose: comparing two UK postcode districts side by side on the Praesago Trajectory Index. It uses a specific verb ('Compare') and resource ('UK postcode districts'), and it distinguishes itself from sibling tools like 'get_area_score' (single district) and 'search_areas' (search, not compare).
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 no guidance on when to use this tool versus alternatives. It does not specify when to use 'compare_areas' over 'get_area_score' or 'get_area_context', nor does it mention any prerequisites or exclusions. The usage is implied but not explicitly clarified.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch full Praesago area intelligence for a postcode district returned by search.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Postcode district id from search results |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds 'full area intelligence' but does not disclose additional behavioral traits beyond what annotations provide. No contradiction.
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, efficient sentence that conveys the tool's purpose without redundancy. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no output schema, the description's 'full area intelligence' provides a reasonable hint of return value. However, it is vague and could benefit from specifying the type of intelligence returned. Overall adequate for a simple 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 100%, so baseline is 3. The description does not add meaning beyond the schema's 'postcode district id from search results'. It is consistent but adds no extra value.
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 fetches 'full Praesago area intelligence' for a postcode district, with the source specified as 'returned by search'. It distinguishes from siblings like get_area_score and get_area_context which are more specific.
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 usage after a search operation, but does not explicitly state when to avoid using this tool or mention alternatives. Sibling tool names provide context, but the description could be more explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Full area intelligence for a UK postcode district: demographics, income, housing tenure, dwelling types, deprivation (IMD), health, education, broadband, flood risk, council tax, politics, and 12-month property prices. Requires a Praesago API key for full data; returns the score summary otherwise.
| Name | Required | Description | Default |
|---|---|---|---|
| area | Yes | UK postcode district, e.g. "E17" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and non-destructive behavior. The description adds important behavioral context about API key requirements and the fallback behavior (score summary without key), which is not captured by 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 two sentences long, front-loads the purpose with a comprehensive list of outputs, and then states the usage condition. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description enumerates the wide range of data returned (demographics, income, etc.) and notes the fallback when no API key is provided. This adequately sets expectations, though a structured list or format hint would improve completeness.
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 only parameter 'area' is described in the schema as a UK postcode district with an example ('E17'). The description reinforces this usage context, adding value beyond the schema's type definition.
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 provides 'full area intelligence for a UK postcode district' and lists numerous specific data categories. It distinguishes itself from siblings like 'get_area_score' by implying a more comprehensive result.
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 explains that a Praesago API key is needed for full data, otherwise a score summary is returned. This provides clear context on when to use the tool effectively, though it does not explicitly contrast with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the Praesago Trajectory Score (0-100) for a UK postcode district or full postcode. Measures the strength of signals that have historically preceded property price growth (business formation, planning activity, crime trends, demand pressure and 30+ more government data sources). Includes national percentile, gentrification early-warning stage, and measured historical calibration. England & Wales coverage.
| Name | Required | Description | Default |
|---|---|---|---|
| area | Yes | UK postcode district (e.g. "E17") or full postcode (e.g. "NN8 3FN") |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds valuable context about the score components (national percentile, gentrification stage, calibration) without contradicting 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 concise paragraph that conveys all necessary information without fluff. It could be slightly more structured but remains efficient.
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?
No output schema exists, but the description explains the score range (0-100), components, and historical calibration, giving the agent a good understanding of what the tool returns.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a detailed description of the 'area' parameter. The description reinforces this with examples but doesn't add new semantic meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves a Praesago Trajectory Score (0-100) for UK postcode districts or full postcodes, distinguishing it from sibling tools like get_area_context, search_areas, and compare_areas.
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 indicates when to use (to measure property price growth signals) and specifies coverage (England & Wales), but doesn't mention when not to use or provide explicit exclusions relative to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Live asking-market state for a UK postcode district, collected weekly from property portals: active listing count, new listings, how many have been price-reduced, median asking price, and median time on market. Answers "is this area moving or stuck?" and "are sellers cutting prices here?" — the negotiation-side data that completed-sales records (which lag by months) cannot show. England & Wales, sale listings.
| Name | Required | Description | Default |
|---|---|---|---|
| area | Yes | UK postcode district, e.g. "E17" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds valuable behavioral context: 'collected weekly from property portals,' indicating the data freshness and source. It also enumerates the key metrics returned, providing full transparency beyond the annotations without contradiction.
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 long, front-loaded with the tool's purpose and key details. Every word adds value—no fluff, no redundancy. It efficiently conveys scope, data points, and use case.
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 tool with one parameter, no output schema, and informative annotations, the description is comprehensive. It explains what data is returned (listing counts, median prices, time on market), the update frequency (weekly), geographic coverage (England & Wales), and the rationale for using this over completed-sales data. It fully equips an agent to understand the tool's purpose and 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?
The single parameter 'area' is well-described in the schema as a UK postcode district with example 'E17'. The description does not significantly add meaning beyond the schema, but the example is helpful. Given 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves live asking-market state for a UK postcode district, listing specific data points (active listings, new listings, price reductions, median price, time on market). It answers key questions about market movement and seller pricing, and the name 'get_market_pulse' complements the description. It implicitly distinguishes from siblings like get_area_score and get_area_context which likely provide different scopes of data.
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 contrasts with completed-sales data that lags by months, indicating this tool is for current negotiation-side insights. It notes the geographic scope (England & Wales, sale listings) and provides a clear use case ('is this area moving or stuck?'). However, it could be even stronger by naming specific sibling tools as alternatives or explicitly stating when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Praesago UK property area intelligence by place name, region or postcode district. Returns matching areas with trajectory scores and links.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Place name, region or postcode district |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds that it 'returns matching areas with trajectory scores and links', which provides behavioral context beyond annotations. However, it does not disclose any additional behaviors like pagination or result limits.
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 sentence that front-loads the action and outcome. No wasted words; every part is meaningful and concise.
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 parameter, no output schema, no nested objects), the description covers the search scope and return type. It does not specify response format or result count, but those are partially inferable from the domain. Nearly complete for a straightforward 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 100% for the single parameter, so baseline is 3. The description's phrase 'by place name, region or postcode district' reinforces the schema description without adding new semantics. No additional parameter-level details are provided.
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 specific verb 'Search' and resource 'Praesago UK property area intelligence', and details the types of input (place name, region, postcode district). It clearly distinguishes the tool's purpose from generic search, but lacks explicit differentiation from sibling 'search_areas'. However, the specificity is sufficient for a high score.
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?
No guidance on when to use this tool versus alternatives like 'search_areas' or 'get_area_score'. The description does not mention any exclusions or preferred contexts, leaving the agent without decision framework.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Screen England & Wales postcode districts by Praesago Trajectory Score, region, and median property price. Returns ranked districts with scores, medians and links. Use for questions like "up and coming areas in Greater Manchester under £250k".
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results, up to 25 (default 10) | |
| region | No | Region filter, e.g. "North West", "London" (optional) | |
| min_score | No | Minimum trajectory score 0-100 (default 55) | |
| max_median_price_gbp | No | Maximum 12m median sold price in GBP (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark it as read-only and non-destructive. The description adds that results are ranked with scores, medians, and links, which is useful context 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?
Two sentences with no wasted words. First sentence specifies purpose and output, second gives actionable examples. 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?
With 4 optional parameters and no output schema, the description explains what is returned (ranked districts, scores, medians, links). Missing details like default ordering or pagination but sufficient for typical use cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema fully documents parameters. The description reinforces the meaning of 'min_score', 'region', and 'max_median_price_gbp' but does not add new semantic detail beyond what the schema provides.
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 ('Screen') and resources ('England & Wales postcode districts') with clear filters. It distinguishes from siblings like 'get_area_score' (single area) by specifying multi-result ranking and links.
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?
Examples of when to use are given ('up and coming areas in Greater Manchester under £250k'), but no explicit exclusion criteria or alternatives compared to siblings. Context is clear but not exhaustive.
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. Dates show when Glama detected each change.
compare_areasfetchget_area_contextget_area_scoreget_market_pulsesearchsearch_areasTools are mostly distinct, with get_area_context and fetch potentially overlapping (both return full area intelligence). However, fetch is specifically for postcode districts from search results, so they are differentiated by usage context.
Naming convention is mixed: three tools use get_ prefix, while others use bare verbs (search, compare_areas, fetch) or a different prefix (search_areas). This inconsistency can be confusing for an agent.
With 7 tools covering core operations (score, context, search, compare, market pulse), the count is well-scoped for the domain of UK property area intelligence.
The tool surface covers key functionalities: scoring, detailed context, search, comparison, and live market data. Minor gap: no tool for historical trends or user-specific data input.
UK area & property intelligence for AI agents: reports, EPC, comparables, with source provenance.
UK neighbourhood research from government open data: postcode reports, comparisons, 45 datasets.
UK property research tools - crime stats, schools, demographics, valuations for AI.
Neighbourhood intelligence for any address: livability scores, community and street data.
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