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Weak Lensing Surveys MCP Server

Weak Lensing Surveys MCP Server

by nesar-ai-assistant
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Python
Remote

weak-lensing-mcp-server

MCP server ("Weak Lensing Surveys MCP Server", tool prefix wl) for the public weak-lensing data vectors, redshift distributions, covariances and chains of DES Y3, KiDS-1000, KiDS-Legacy and HSC Y3 (DES Y6: registered, not yet public). It is part of the cosmo-*-mcp family, built from cosmo-mcp-template.

Datasets

key

products

access

source (verified 2026-11-04)

des_y3

xi+-, gamma_t, w(theta), n(z) source+lens, 1000x1000 cov; ~30 chains

download (28 MB FITS, 5-50 MB chains)

desdr-server.ncsa.illinois.edu/despublic/y3a2_files/{datavectors,chains}/

des_y6

-

unreleased (y6key_products/ is empty)

desdr-server.ncsa.illinois.edu/despublic/y6a2_files/

kids_1000

xi+-, COSEBIs, band powers, n(z), cov; MultiNest chains

download (17 MB tarball)

kids.strw.leidenuniv.nl/DR4/data_files/KiDS1000_cosmic_shear_data_release.tgz

kids_legacy

xi+-, COSEBIs E/B, band powers E/B, n(z), cov; Nautilus chains

download (131 MB tarball)

kids.strw.leidenuniv.nl/sci_data/KiDS_Legacy_cosmic_shear_data_release.tar.gz

hsc_y3

real-space xi+- (Li+23), Fourier C_ell SACC (Dalal+23), n(z), cov

download (CosmoSIS standard library mirror); chains credentialed

raw.githubusercontent.com/cosmosis-developers/cosmosis-standard-library/main/likelihood/hsc_cosmic_shear/

Notes: the KiDS server's TLS chain fails with some Python default CA stores, so downloads use a certifi-backed opener inside tools/datavectors/io.py. The HSC portal (hsc-release.mtk.nao.ac.jp) returns HTTP 401 without HSC-SSP credentials. HSC chains therefore raise an error with manual-download steps. Save the chain as ~/.cache/cosmo-mcp/weak-lensing-mcp-server/hsc_y3/<chain>.txt and the tools pick it up. Nothing is bundled; the cache is ~/.cache/cosmo-mcp/weak-lensing-mcp-server ($COSMO_MCP_DATA).

Related MCP server: spectra-mcp-server

Tools

  • fetch_wl_datavector(dataset, statistic) - tidy CSV (bin1, bin2, angbin, x, value, error) + covariance .npy

  • plot_shear_correlation - xi+ / xi- triangle of bin pairs (DES/KiDS paper style)

  • plot_redshift_distributions - n(z) per tomographic bin, multi-survey

  • plot_covariance - correlation matrix, PD check, condition number

  • compute_signal_to_noise - sqrt(d^T C^-1 d) with cross-covariance

  • fetch_wl_chain - published chain -> slim CSV (weight, Om, sigma8, S8, h0, w)

  • analyze_wl_chain (heavy, dispatchable kernel chains.kernels.chain_marginals) - marginals, mode/HPD

  • plot_s8_om_contours - multi-survey 68/95% contours with Planck band

  • compare_s8_constraints - S8 whisker plot vs Planck 2018 (0.832 +/- 0.013) and tension in sigma

  • meta: list_wl_datasets, describe_wl_dataset, list_wl_skills, load_wl_skill; dispatch tools from the template

Skills: wl-tour, s8-tension-across-surveys, reproduce-des-y3-shear-figure, wl-datavector-inspection, hpc-dispatch-handoff.

Sanity numbers (this server, from the public chains)

chain

S8 (mean +/- std)

paper

DES Y3 3x2pt LCDM

0.776 +/- 0.018

0.776 +/- 0.017

KiDS-1000 COSEBIs

0.751 +/- 0.022 (mode 0.760, max-post 0.760)

0.759 +0.024/-0.021 (MAP, PJ-HPD)

KiDS-Legacy COSEBIs

0.814 +/- 0.019

0.813 +/- 0.018 (mean)

HSC Y3 real space

credentialed chain - published value used

0.769 +0.031/-0.034

DES Y3 xi+- S8 vs Planck S8-Om

Install / run

uv venv --python 3.12 .venv && uv pip install --python .venv/bin/python -e ".[dev]"
.venv/bin/python -m mcp_server --transport stdio            # or ./run_server.sh
.venv/bin/python -m mcp_server --transport streamable-http   # port 8013

Optional extra: getdist (not required; contours use a weighted smoothed histogram). sacc is a core dependency (HSC Fourier C_ell + n(z)). Tests: MPLBACKEND=Agg .venv/bin/python -m pytest -q (-m "not network" offline).

This server cannot be deployed

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Maintenance

ActivityMaintained
ResponsivenessNo issues

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